[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3498":3,"related-3498":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":24,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":85},3498,"DRON VA MASOFADAN MONITORING TEXNOLOGIYALARIGA OID AGRAR TERMINLARNING INGLIZ TILIDAN O'ZBEK TILIGA KIRIB KELISH MEXANIZMLARI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22942951","Ushbu maqolada dron va masofadan monitoring texnologiyalari bilan bog‘liq zamonaviy agrar terminlarning ingliz tilidan o‘zbek tiliga kirib kelish mexanizmlari leksik-semantik va tarjimashunoslik nuqtayi nazaridan tahlil qilinadi. Tadqiqot materiali sifatida precision agriculture, drone, unmanned aerial vehicle, remote sensing, crop monitoring, multispectral imaging, hyperspectral imaging, thermal imaging, vegetation index, orthomosaic, georeferencing, payload, flight planning, variable-rate application singari xalqaro ilmiy-texnik birliklar va ularning o‘zbekcha qo‘llanish shakllari tanlandi. Tahlil natijasida o‘zlashishning besh asosiy modeli: bevosita fonetik-grafik o‘zlashtirish, kalkalash, tavsifiy-ekvivalent tarjima, qisqartmalarni saqlash hamda gibrid termin hosil qilish faol ekanligi aniqlandi. Agrar raqamlashtirish tezlashgani sari inglizcha terminlarning o‘zbek tiliga kirib kelishi faqat lug‘aviy jarayon emas, balki texnologik transfer, professional kommunikatsiya va terminologik me’yorlash bilan uzviy bog‘liq jarayon sifatida namoyon bo‘lmoqda. Maqolada variantdorlikni kamaytirish, o‘zbek tilining so‘z yasash imkoniyatlaridan oqilona foydalanish va xalqaro qisqartmalar bilan milliy ekvivalentlar o‘rtasida funksional muvozanatni ta’minlash bo‘yicha takliflar ilgari suriladi.","本文从词汇语义学和翻译学的角度，分析了与无人机和遥感监测技术相关的现代涉农术语从英语进入乌兹别克语的机制。研究以precision agriculture（精准农业）、drone（无人机）、unmanned aerial vehicle（无人驾驶飞行器）、remote sensing（遥感）、crop monitoring（作物监测）、multispectral imaging（多光谱成像）、hyperspectral imaging（高光谱成像）、thermal imaging（热成像）、vegetation index（植被指数）、orthomosaic（正射影像图）、georeferencing（地理配准）、payload（有效载荷）、flight planning（飞行规划）、variable-rate application（变量施用）等国际科技统一术语及其乌兹别克语使用形式为材料。分析结果表明，借入的五种主要模式较为活跃：直接语音—字形借入、仿译、描述—等值翻译、保留缩略语以及混合术语构词。随着农业数字化加速，英语术语进入乌兹别克语不仅是一个词汇过程，而且表现为与工艺转移、专业交流和术语规范化密切相关的过程。文中就减少变体现象、合理利用乌兹别克语的构词能力以及在国标缩略语与民族语对应词之间保持功能平衡提出了建议。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-24T00:00:00Z","论文",25,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,15,12,9,1,"论文系统分析无人机与遥感农业术语从英语进入乌兹别克语的五种借入机制，属农业信息化语言标准化研究，专业性强但影响面偏窄。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22942950",[29,30,31,32,33],"智慧农业","无人机","农业遥感","农业术语","乌兹别克语",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,57],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":56},10,true,[],[52,53,54,55],"遥感","remote sensing","multispectral","hyperspectral",[31],{"code":58,"score":59,"supported":49,"title":60,"abstract":61,"tags":64},"I1",7,[],[62,63],"精准农业","precision agriculture",[29],[66,67],"乌兹别克语 农业术语 农业遥感 智慧农业","乌兹别克语 农业术语","乌兹别克语农业术语农业遥感智慧农业-3498",3,"10.5281\u002Fzenodo.22942951",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":76,"oa_url":6,"card":77,"direction":83,"ingested_from":84},"W7214177750",[74],{"name":75,"orcid":9},"Qodirova Gulbahor Turdiyevna",0,{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"分析无人机与遥感农业术语从英语借入乌兹别克语的五种机制。","选取精准农业等15个术语，做词汇语义与翻译学分析。","借入机制含音译、仿译、描述翻译、缩写保留和混合构词五类。","数字乡村与农业信息化","可构建多语言农业术语库，研究术语标准化对技术扩散的影响。","农业遥感与作物表型","openalex","2026-09-25T23:30:30.787630Z",{"total":87,"page":22,"page_size":87,"items":88},6,[89,144,222,294,371,460],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":9,"content":9,"source_name":94,"source_url":92,"published_at":95,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":96,"score_detail":97,"sources":101,"tags":103,"primary_topic":106,"secondary_topics":107,"classification":108,"search_phrases":125,"slug":128,"view_count":69,"doi":129,"paper":130,"created_at":143},3135,"Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10453-3","Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework。Precision Agriculture","Precision Agriculture","2026-09-22T00:00:00Z",82,{"impact":18,"substance":98,"depth":18,"authority":99,"freshness":48,"relevant":22,"comment":100},22,14,"方法新颖、数据可靠，对草地精准施肥有参考价值，但属细分领域研究，未达重大突破层级。",[102],{"name":94,"url":92},[29,30,31,104,105],"机器学习","草地氮素",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":109},{"candidates":110,"agricultural_evidence":49},[111,115,120],{"code":35,"score":20,"supported":49,"title":112,"abstract":113,"tags":114},[54],[54],[31],{"code":58,"score":116,"supported":49,"title":117,"abstract":118,"tags":119},4,[],[63],[29],{"code":121,"score":22,"supported":14,"title":122,"abstract":123,"tags":124},"I2",[],[],[104],[126,127],"UAV 多光谱 草地 氮素","Precision Agriculture 氮素估算","UAV多光谱草地氮素-3135","10.1007\u002Fs11119-026-10453-3",{"doi":129,"openalex_id":131,"authors":132,"venue":94,"cited_by_count":76,"oa_url":9,"card":9,"direction":9,"ingested_from":84},"W7213942551",[133,135,138,141],{"name":134,"orcid":9},"Antônio de Oliveira Costa Neto",{"name":136,"orcid":137},"Yiannis Ampatzidis","https:\u002F\u002Forcid.org\u002F0000-0002-3660-3298",{"name":139,"orcid":140},"Andrea Lazzari","https:\u002F\u002Forcid.org\u002F0000-0002-1521-6942",{"name":142,"orcid":9},"Jim Fletcher","2026-09-22T23:30:03.351447Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":9,"source_name":150,"source_url":147,"published_at":151,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":152,"score_detail":153,"sources":155,"tags":157,"primary_topic":160,"secondary_topics":161,"classification":166,"search_phrases":199,"slug":202,"view_count":116,"doi":203,"paper":204,"created_at":221},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。","Computers and Electronics in Agriculture","2026-09-18T00:00:00Z",81,{"impact":18,"substance":98,"depth":18,"authority":19,"freshness":17,"relevant":22,"comment":154},"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。",[156],{"name":150,"url":147},[29,30,158,31,159],"农业人工智能","作物倒伏",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[162],{"code":121,"slug":163,"name":164,"path":165,"parent_code":39},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":42,"locked":14,"version":43,"method":44,"evidence":167},{"candidates":168,"agricultural_evidence":49},[169,173,180,185,192],{"code":35,"score":18,"supported":49,"title":170,"abstract":171,"tags":172},[53],[52,53,54,55],[31],{"code":121,"score":48,"supported":49,"title":174,"abstract":175,"tags":179},[],[176,104,177,178],"深度学习","machine learning","deep learning",[158],{"code":58,"score":59,"supported":49,"title":181,"abstract":182,"tags":184},[],[29,183],"smart agriculture",[29],{"code":186,"score":87,"supported":49,"title":187,"abstract":188,"tags":191},"A8",[],[189,190],"粮食安全","food security",[],{"code":193,"score":87,"supported":49,"title":194,"abstract":195,"tags":198},"J3",[],[196,197],"全球农业","global agriculture",[],[200,201],"无人机 遥感 作物倒伏","UAV 倒伏 监测","无人机遥感作物倒伏-2906","10.1016\u002Fj.compag.2026.112446",{"doi":203,"openalex_id":205,"authors":206,"venue":150,"cited_by_count":76,"oa_url":147,"card":215,"direction":220,"ingested_from":84},"W7213542473",[207,210,212],{"name":208,"orcid":209},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":211,"orcid":9},"Changxing Geng",{"name":213,"orcid":214},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":216,"method":217,"finding":218,"direction":83,"opportunity":219},"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。","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","2026-09-19T23:30:01.740139Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":227,"content":9,"source_name":228,"source_url":225,"published_at":229,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":230,"score_detail":231,"sources":234,"tags":236,"primary_topic":239,"secondary_topics":244,"classification":246,"search_phrases":264,"slug":267,"view_count":268,"doi":269,"paper":270,"created_at":293},2663,"Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2061","Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining \"SAM + MobileNetV3-Small classification\" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.","准确监测水稻地上生物量(AGB)对指导农业生产管理至关重要。本研究聚焦水稻AGB的高精度估算与自动化制图。田间试验在浙江省湖州市南浔区开展，采集了无人机RGB和多光谱影像、水稻AGB及株高数据。通过融合植被指数、纹理特征和株高信息，利用Stacking等算法构建AGB估算模型。提出了一种“SAM+MobileNetV3-Small分类”框架，以实现稻田自动化提取和物候识别。结果表明，基于Stacking集成算法的水稻AGB预测模型表现优异，株高的引入显著提高了模型精度。例如，在抽穗期，R2从0.421提升至0.739，RPIQ从1.995提升至3.015。本研究开发的稻田自动化提取与物候识别框架在本研究数据集上实现了0.968的分割精度和0.993的分类精度，且无需人工标注。本研究为水稻AGB的自动化高精度制图提供了技术参考。","Journal of Agricultural Engineering","2026-09-15T00:00:00Z",78,{"impact":232,"substance":98,"depth":18,"authority":99,"freshness":17,"relevant":22,"comment":233},16,"基于无人机遥感与Stacking集成模型实现水稻地上生物量自动化制图，方法新颖、数据扎实，对精准农业管理有参考价值。",[235],{"name":228,"url":225},[29,30,31,237,238],"水稻","作物表型",{"code":240,"slug":241,"name":237,"path":242,"parent_code":243},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[245],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":247},{"candidates":248,"agricultural_evidence":49},[249,255,260],{"code":240,"score":18,"supported":49,"title":250,"abstract":251,"tags":254},[241],[237,252,241,253],"稻田","paddy",[237],{"code":35,"score":256,"supported":49,"title":257,"abstract":258,"tags":259},13,[53],[54],[238,31],{"code":58,"score":22,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[29],[265,266],"作物表型 农业遥感 智慧农业 无人机","作物表型 农业遥感","作物表型农业遥感智慧农业无人机-2663",2,"10.4081\u002Fjae.2026.2061",{"doi":269,"openalex_id":271,"authors":272,"venue":228,"cited_by_count":76,"oa_url":225,"card":288,"direction":83,"ingested_from":84},"W7213229220",[273,275,277,279,281,284,286],{"name":274,"orcid":9},"Honggang Xu",{"name":276,"orcid":9},"Xuehan Li",{"name":278,"orcid":9},"Jia Shen",{"name":280,"orcid":9},"Ziyi Li",{"name":282,"orcid":283},"Zhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-6496-3697",{"name":285,"orcid":9},"Yiming Li",{"name":287,"orcid":9},"Pengcheng Nie",{"tldr":289,"method":290,"finding":291,"direction":83,"opportunity":292},"基于无人机RGB与多光谱影像，结合株高与Stacking集成算法，实现水稻地上生物量高精度自动制图。","无人机RGB\u002F多光谱影像、植被指数、纹理与株高，Stacking集成及SAM+M","引入株高显著提升精度，抽穗期R²从0.421升至0.739；自动稻田提取与物候识别精度达0.968和","可探索多生育期、多品种下株高与纹理特征的迁移性，并耦合深度学习实现全自动生物量时空制图。","2026-09-16T23:30:28.929376Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":150,"source_url":297,"published_at":229,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":300,"score_detail":301,"sources":303,"tags":305,"primary_topic":308,"secondary_topics":314,"classification":316,"search_phrases":335,"slug":338,"view_count":116,"doi":339,"paper":340,"created_at":370},2617,"Dynamic ROI-constrained UAV multispectral estimation of single-plant LAI in trellis-trained kiwifruit","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112437","Leaf area index (LAI) is a key biophysical parameter for characterizing orchard canopy structure and growth status, and it is closely associated with crop productivity. In trellis-trained orchards, severe branch overlap and persistent background interference make it difficult to delineate single-plant observation boundaries, while radiometric drift across multi-temporal observations further reduces feature comparability. To address these issues, this study proposes a dynamic ROI-constrained framework for single-plant LAI estimation from UAV multispectral imagery. First, relative radiometric calibration based on pseudo-invariant features was applied to reduce inter-date spectral bias and establish a unified radiometric baseline for multi-temporal analysis. Second, a dynamic ROI extraction algorithm was developed using dual constraints from the spatial distance percentile (P) and the local spectral purity threshold (Q). Parameter ablation results showed that the P90_Q70 combination achieved the best balance between retaining core canopy information and suppressing background noise. Compared with the best fixed-radius strategy (3.00 m), this scheme improved R 2 from 0.700 to 0.766 and reduced RMSE to 0.122. On this basis, spectral vegetation indices and near-infrared texture features were integrated to construct a multidimensional feature set. The gradient boosting decision tree (GBDT) model achieved the best predictive performance, with R 2 = 0.772, RMSE = 0.120, and RPD = 2.094, and was further used to generate high-resolution LAI maps of the orchard. These results indicate that the combination of dynamic boundary constraints and spectral purity screening can improve the robustness of single-plant feature extraction in trellis-trained kiwifruit orchards and provide a practical reference for quantitative remote sensing of other densely closed orchard systems.","叶面积指数（LAI）是表征果园冠层结构与生长状态的关键生物物理参数，与作物生产力密切相关。在棚架式栽培果园中，严重的枝条重叠和持续的背景干扰使得单株观测边界难以界定，而多时相观测间的辐射漂移进一步降低了特征可比性。为解决上述问题，本研究提出了一种基于无人机多光谱影像的单株LAI估算动态ROI约束框架。首先，采用基于伪不变特征的相对辐射校正以减小日期间的光谱偏差，为多时相分析建立统一的辐射基准。其次，利用空间距离百分位数（P）和局部光谱纯度阈值（Q）的双重约束，开发了动态ROI提取算法。参数消融结果表明，P90_Q70组合在保留核心冠层信息与抑制背景噪声之间取得了最佳平衡。与最优固定半径策略（3.00 m）相比，该方案将R²从0.700提升至0.766，并将RMSE降至0.122。在此基础上，融合光谱植被指数与近红外纹理特征构建多维特征集。梯度提升决策树（GBDT）模型取得了最佳预测性能，R²=0.772，RMSE=0.120，RPD=2.094，并进一步用于生成果园高分辨率LAI分布图。上述结果表明，动态边界约束与光谱纯度筛选相结合可提高棚架式猕猴桃果园单株特征提取的鲁棒性，并为其他密集郁闭果园系统的定量遥感提供实践参考。",79,{"impact":232,"substance":98,"depth":18,"authority":99,"freshness":21,"relevant":22,"comment":302},"提出动态ROI约束与辐射校正结合的无人机多光谱单株LAI估测框架，方法新颖、指标提升明确，对密植果园定量遥感有实用参考价值。",[304],{"name":150,"url":297},[29,30,31,306,307],"猕猴桃","叶面积指数",{"code":309,"slug":310,"name":311,"path":312,"parent_code":313},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[315],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":317},{"candidates":318,"agricultural_evidence":49},[319,325,331],{"code":35,"score":320,"supported":49,"title":321,"abstract":322,"tags":324},19,[54],[52,323,53,54],"无人机多光谱",[31,307],{"code":309,"score":18,"supported":49,"title":326,"abstract":328,"tags":330},[327],"kiwifruit",[306,329,327],"orchard",[306],{"code":58,"score":22,"supported":14,"title":332,"abstract":333,"tags":334},[],[],[29],[336,337],"叶面积指数 农业遥感 智慧农业 无人机","叶面积指数 农业遥感","叶面积指数农业遥感智慧农业无人机-2617","10.1016\u002Fj.compag.2026.112437",{"doi":339,"openalex_id":341,"authors":342,"venue":150,"cited_by_count":76,"oa_url":297,"card":365,"direction":83,"ingested_from":84},"W7213243341",[343,345,347,349,352,354,356,358,360,363],{"name":344,"orcid":9},"Wenjie Li",{"name":346,"orcid":9},"Hongen Liu",{"name":348,"orcid":9},"Linghuan Ouyang",{"name":350,"orcid":351},"Qian Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7916-1370",{"name":353,"orcid":9},"Tianqi Lv",{"name":355,"orcid":9},"Jiali Li",{"name":357,"orcid":9},"Xintao Lin",{"name":359,"orcid":9},"Chen Yao",{"name":361,"orcid":362},"Yongqiang Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-3246-2800",{"name":364,"orcid":9},"Jianping Qian",{"tldr":366,"method":367,"finding":368,"direction":83,"opportunity":369},"提出动态ROI约束框架，用无人机多光谱影像估算棚架猕猴桃单株LAI。","伪不变特征辐射校正、动态ROI提取（P90_Q70）、植被指数与纹理特征、GBD","动态ROI优于固定半径，GBDT最优（R²=0.772，RMSE=0.120，RPD=2.094）。","可迁移至其他密植果园，探索多时相辐射校正与动态边界约束的普适性及轻量化模型。","2026-09-16T23:30:02.136941Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":376,"content":9,"source_name":377,"source_url":374,"published_at":378,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":379,"score_detail":380,"sources":382,"tags":384,"primary_topic":387,"secondary_topics":392,"classification":400,"search_phrases":428,"slug":431,"view_count":76,"doi":432,"paper":433,"created_at":459},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":232,"substance":98,"depth":18,"authority":99,"freshness":48,"relevant":22,"comment":381},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[383],{"name":377,"url":374},[29,158,31,385,386],"氮素监测","紫花苜蓿",{"code":388,"slug":389,"name":390,"path":391,"parent_code":243},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping",[393,394],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":395,"slug":396,"name":397,"path":398,"parent_code":399},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":42,"locked":14,"version":43,"method":44,"evidence":401},{"candidates":402,"agricultural_evidence":49},[403,407,413,419,424],{"code":35,"score":18,"supported":49,"title":404,"abstract":405,"tags":406},[55],[52,53,55],[31],{"code":388,"score":19,"supported":49,"title":408,"abstract":410,"tags":412},[409],"alfalfa",[411,409],"苜蓿",[386],{"code":395,"score":48,"supported":49,"title":414,"abstract":415,"tags":418},[],[416,411,417],"饲草","forage",[386],{"code":121,"score":48,"supported":49,"title":420,"abstract":421,"tags":423},[],[176,104,177,178,422],"decision support",[158],{"code":58,"score":22,"supported":14,"title":425,"abstract":426,"tags":427},[],[],[29],[429,430],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":432,"openalex_id":434,"authors":435,"venue":377,"cited_by_count":76,"oa_url":374,"card":454,"direction":83,"ingested_from":84},"W7223154576",[436,438,440,442,444,447,449,452],{"name":437,"orcid":9},"Shuai Fu",{"name":439,"orcid":9},"Yunhao Li",{"name":441,"orcid":9},"Chunli Miao",{"name":443,"orcid":9},"Jie Liu",{"name":445,"orcid":446},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":448,"orcid":9},"Yongjia Hu",{"name":450,"orcid":451},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":453,"orcid":9},"吴彩霞",{"tldr":455,"method":456,"finding":457,"direction":83,"opportunity":458},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":461,"title":462,"url":463,"summary":464,"summary_zh":465,"content":9,"source_name":466,"source_url":463,"published_at":467,"category":12,"cover_url":9,"hotness":48,"is_selected":14,"score":300,"score_detail":468,"sources":470,"tags":472,"primary_topic":474,"secondary_topics":475,"classification":477,"search_phrases":496,"slug":499,"view_count":76,"doi":500,"paper":501,"created_at":543},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",{"impact":18,"substance":98,"depth":18,"authority":20,"freshness":21,"relevant":22,"comment":469},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[471],{"name":466,"url":463},[29,30,473,52,386],"育种",{"code":388,"slug":389,"name":390,"path":391,"parent_code":243},[476],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":478},{"candidates":479,"agricultural_evidence":49},[480,484,488,492],{"code":388,"score":19,"supported":49,"title":481,"abstract":482,"tags":483},[409],[411,409],[386],{"code":35,"score":20,"supported":49,"title":485,"abstract":486,"tags":487},[54],[54],[52],{"code":58,"score":59,"supported":49,"title":489,"abstract":490,"tags":491},[],[62,63],[29],{"code":395,"score":116,"supported":49,"title":493,"abstract":494,"tags":495},[],[411],[386],[497,498],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":500,"openalex_id":502,"authors":503,"venue":466,"cited_by_count":76,"oa_url":463,"card":538,"direction":83,"ingested_from":84},"W7223083966",[504,507,510,513,516,518,520,523,526,529,532,535],{"name":505,"orcid":506},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":508,"orcid":509},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":511,"orcid":512},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":514,"orcid":515},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":517,"orcid":9},"Julie Hansen",{"name":519,"orcid":9},"Christopher Pierce",{"name":521,"orcid":522},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":524,"orcid":525},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":527,"orcid":528},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":530,"orcid":531},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":533,"orcid":534},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":536,"orcid":537},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":539,"method":540,"finding":541,"direction":83,"opportunity":542},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z"]