[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3135":3,"related-3135":83},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":21,"tags":23,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":82},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",null,"Precision Agriculture","2026-09-22T00:00:00Z","论文",10,false,82,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":12,"relevant":19,"comment":20},18,22,14,1,"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。",[22],{"name":9,"url":6},[24,25,26,27,28],"智慧农业","无人机","农业遥感","机器学习","草地氮素",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":37,"locked":13,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,56],{"code":30,"score":43,"supported":44,"title":45,"abstract":47,"tags":48},12,true,[46],"multispectral",[46],[26],{"code":50,"score":51,"supported":44,"title":52,"abstract":53,"tags":55},"I1",4,[],[54],"precision agriculture",[24],{"code":57,"score":19,"supported":13,"title":58,"abstract":59,"tags":60},"I2",[],[],[27],[62,63],"UAV 多光谱 草地 氮素","Precision Agriculture 氮素估算","UAV多光谱草地氮素-3135",3,"10.1007\u002Fs11119-026-10453-3",{"doi":66,"openalex_id":68,"authors":69,"venue":9,"cited_by_count":80,"oa_url":8,"card":8,"direction":8,"ingested_from":81},"W7213942551",[70,72,75,78],{"name":71,"orcid":8},"Antônio de Oliveira Costa Neto",{"name":73,"orcid":74},"Yiannis Ampatzidis","https:\u002F\u002Forcid.org\u002F0000-0002-3660-3298",{"name":76,"orcid":77},"Andrea Lazzari","https:\u002F\u002Forcid.org\u002F0000-0002-1521-6942",{"name":79,"orcid":8},"Jim Fletcher",0,"openalex","2026-09-22T23:30:03.351447Z",{"total":84,"page":19,"page_size":84,"items":85},6,[86,144,240,314,379,456],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":8,"source_name":92,"source_url":89,"published_at":93,"category":11,"cover_url":8,"hotness":94,"is_selected":13,"score":95,"score_detail":96,"sources":101,"tags":105,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":126,"slug":129,"view_count":65,"doi":130,"paper":131,"created_at":143},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-24T00:00:00Z",25,62,{"impact":97,"substance":16,"depth":98,"authority":43,"freshness":99,"relevant":19,"comment":100},8,15,9,"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。",[102,103],{"name":92,"url":89},{"name":92,"url":104},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22942950",[24,25,26,106,107],"农业术语","乌兹别克语",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":13,"version":38,"method":39,"evidence":111},{"candidates":112,"agricultural_evidence":44},[113,120],{"code":30,"score":12,"supported":44,"title":114,"abstract":115,"tags":119},[],[116,117,46,118],"遥感","remote sensing","hyperspectral",[26],{"code":50,"score":121,"supported":44,"title":122,"abstract":123,"tags":125},7,[],[124,54],"精准农业",[24],[127,128],"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","10.5281\u002Fzenodo.22942951",{"doi":130,"openalex_id":132,"authors":133,"venue":92,"cited_by_count":80,"oa_url":89,"card":136,"direction":142,"ingested_from":81},"W7214177750",[134],{"name":135,"orcid":8},"Qodirova Gulbahor Turdiyevna",{"tldr":137,"method":138,"finding":139,"direction":140,"opportunity":141},"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","2026-09-25T23:30:30.787630Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":8,"source_name":150,"source_url":147,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":151,"score_detail":152,"sources":156,"tags":158,"primary_topic":161,"secondary_topics":166,"classification":172,"search_phrases":193,"slug":196,"view_count":97,"doi":197,"paper":198,"created_at":239},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估","Environmental Monitoring and Assessment",64,{"impact":97,"substance":16,"depth":153,"authority":154,"freshness":99,"relevant":19,"comment":155},16,13,"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[157],{"name":150,"url":147},[24,26,159,27,160],"水稻","高光谱遥感",{"code":162,"slug":163,"name":159,"path":164,"parent_code":165},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[167,168],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"code":57,"slug":169,"name":170,"path":171,"parent_code":34},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":37,"locked":13,"version":38,"method":39,"evidence":173},{"candidates":174,"agricultural_evidence":44},[175,180,185,189],{"code":30,"score":176,"supported":44,"title":177,"abstract":178,"tags":179},27,[117,118],[116,117,118],[26,160],{"code":57,"score":98,"supported":44,"title":181,"abstract":183,"tags":184},[182],"machine learning",[27,182],[27],{"code":162,"score":43,"supported":44,"title":186,"abstract":187,"tags":188},[163],[163],[159],{"code":50,"score":19,"supported":13,"title":190,"abstract":191,"tags":192},[],[],[24],[194,195],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278","10.1007\u002Fs10661-026-15959-x",{"doi":197,"openalex_id":199,"authors":200,"venue":150,"cited_by_count":80,"oa_url":8,"card":234,"direction":142,"ingested_from":81},"W7214027889",[201,204,207,210,213,216,218,220,223,226,228,230,232],{"name":202,"orcid":203},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":205,"orcid":206},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":208,"orcid":209},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":211,"orcid":212},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":214,"orcid":215},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":217,"orcid":8},"Ying Luo",{"name":219,"orcid":8},"Jiaqian Zhang",{"name":221,"orcid":222},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":224,"orcid":225},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":227,"orcid":8},"Chaoliang Peng",{"name":229,"orcid":8},"Duan Tian",{"name":231,"orcid":8},"Weihao Wang",{"name":233,"orcid":8},"Jiaxin Liu",{"tldr":235,"method":236,"finding":237,"direction":142,"opportunity":238},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","2026-09-23T23:30:19.291361Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":8,"source_name":246,"source_url":243,"published_at":247,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":248,"score_detail":249,"sources":251,"tags":253,"primary_topic":256,"secondary_topics":257,"classification":259,"search_phrases":291,"slug":294,"view_count":51,"doi":295,"paper":296,"created_at":313},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":16,"substance":17,"depth":16,"authority":98,"freshness":97,"relevant":19,"comment":250},"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。",[252],{"name":246,"url":243},[24,25,254,26,255],"农业人工智能","作物倒伏",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[258],{"code":57,"slug":169,"name":170,"path":171,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":260},{"candidates":261,"agricultural_evidence":44},[262,266,272,277,284],{"code":30,"score":16,"supported":44,"title":263,"abstract":264,"tags":265},[117],[116,117,46,118],[26],{"code":57,"score":12,"supported":44,"title":267,"abstract":268,"tags":271},[],[269,27,182,270],"深度学习","deep learning",[254],{"code":50,"score":121,"supported":44,"title":273,"abstract":274,"tags":276},[],[24,275],"smart agriculture",[24],{"code":278,"score":84,"supported":44,"title":279,"abstract":280,"tags":283},"A8",[],[281,282],"粮食安全","food security",[],{"code":285,"score":84,"supported":44,"title":286,"abstract":287,"tags":290},"J3",[],[288,289],"全球农业","global agriculture",[],[292,293],"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":295,"openalex_id":297,"authors":298,"venue":246,"cited_by_count":80,"oa_url":243,"card":307,"direction":312,"ingested_from":81},"W7213542473",[299,302,304],{"name":300,"orcid":301},"Dashuai Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3159-7175",{"name":303,"orcid":8},"Changxing Geng",{"name":305,"orcid":306},"Xiaoguang Liu","https:\u002F\u002Forcid.org\u002F0000-0002-0935-3094",{"tldr":308,"method":309,"finding":310,"direction":142,"opportunity":311},"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":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":8,"source_name":320,"source_url":317,"published_at":321,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":322,"score_detail":323,"sources":325,"tags":327,"primary_topic":329,"secondary_topics":330,"classification":332,"search_phrases":349,"slug":352,"view_count":353,"doi":354,"paper":355,"created_at":378},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":153,"substance":17,"depth":16,"authority":18,"freshness":97,"relevant":19,"comment":324},"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。",[326],{"name":320,"url":317},[24,25,26,159,328],"作物表型",{"code":162,"slug":163,"name":159,"path":164,"parent_code":165},[331],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":333},{"candidates":334,"agricultural_evidence":44},[335,341,345],{"code":162,"score":16,"supported":44,"title":336,"abstract":337,"tags":340},[163],[159,338,163,339],"稻田","paddy",[159],{"code":30,"score":154,"supported":44,"title":342,"abstract":343,"tags":344},[117],[46],[328,26],{"code":50,"score":19,"supported":13,"title":346,"abstract":347,"tags":348},[],[],[24],[350,351],"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":354,"openalex_id":356,"authors":357,"venue":320,"cited_by_count":80,"oa_url":317,"card":373,"direction":142,"ingested_from":81},"W7213229220",[358,360,362,364,366,369,371],{"name":359,"orcid":8},"Honggang Xu",{"name":361,"orcid":8},"Xuehan Li",{"name":363,"orcid":8},"Jia Shen",{"name":365,"orcid":8},"Ziyi Li",{"name":367,"orcid":368},"Zhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-6496-3697",{"name":370,"orcid":8},"Yiming Li",{"name":372,"orcid":8},"Pengcheng Nie",{"tldr":374,"method":375,"finding":376,"direction":142,"opportunity":377},"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":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":8,"source_name":246,"source_url":382,"published_at":321,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":385,"score_detail":386,"sources":388,"tags":390,"primary_topic":393,"secondary_topics":399,"classification":401,"search_phrases":420,"slug":423,"view_count":51,"doi":424,"paper":425,"created_at":455},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":153,"substance":17,"depth":16,"authority":18,"freshness":99,"relevant":19,"comment":387},"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。",[389],{"name":246,"url":382},[24,25,26,391,392],"猕猴桃","叶面积指数",{"code":394,"slug":395,"name":396,"path":397,"parent_code":398},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[400],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":402},{"candidates":403,"agricultural_evidence":44},[404,410,416],{"code":30,"score":405,"supported":44,"title":406,"abstract":407,"tags":409},19,[46],[116,408,117,46],"无人机多光谱",[26,392],{"code":394,"score":16,"supported":44,"title":411,"abstract":413,"tags":415},[412],"kiwifruit",[391,414,412],"orchard",[391],{"code":50,"score":19,"supported":13,"title":417,"abstract":418,"tags":419},[],[],[24],[421,422],"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":424,"openalex_id":426,"authors":427,"venue":246,"cited_by_count":80,"oa_url":382,"card":450,"direction":142,"ingested_from":81},"W7213243341",[428,430,432,434,437,439,441,443,445,448],{"name":429,"orcid":8},"Wenjie Li",{"name":431,"orcid":8},"Hongen Liu",{"name":433,"orcid":8},"Linghuan Ouyang",{"name":435,"orcid":436},"Qian Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7916-1370",{"name":438,"orcid":8},"Tianqi Lv",{"name":440,"orcid":8},"Jiali Li",{"name":442,"orcid":8},"Xintao Lin",{"name":444,"orcid":8},"Chen Yao",{"name":446,"orcid":447},"Yongqiang Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-3246-2800",{"name":449,"orcid":8},"Jianping Qian",{"tldr":451,"method":452,"finding":453,"direction":142,"opportunity":454},"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":457,"title":458,"url":459,"summary":460,"summary_zh":461,"content":8,"source_name":462,"source_url":459,"published_at":463,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":464,"score_detail":465,"sources":468,"tags":470,"primary_topic":473,"secondary_topics":474,"classification":476,"search_phrases":504,"slug":507,"view_count":19,"doi":508,"paper":509,"created_at":533},1532,"Analysis of crop residue cover in the North China Plain based on multispectral remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727176","Crop residue cover (CRC) is a critical parameter for evaluating conservation tillage practices, mitigating soil erosion, and developing models for global carbon cycle monitoring. Traditional optical remote sensing index-based methods are significantly affected by variations in soil and crop residue moisture content as well as interference from emerging subsequent crops, resulting in low estimation accuracy and poor stability of CRC. Due to the presence of subsequent crops, the non-photosynthetic vegetation fraction (fNPV) derived from linear spectral mixture analysis is typically lower than the actual field CRC. This study proposes a novel CRC estimation method based on linear spectral mixture analysis. The workflow consists of three main components: (1) development of a Crop Residue Random Forest (CRRF) index using a random forest regression model to simulate the narrowband SINDRI index – which is less sensitive to moisture – from broadband multispectral reflectance; (2) construction of a two-dimensional triangular feature space combining the CRRF and NDVI indices, followed by linear spectral mixture analysis to simultaneously estimate field fNPV, photosynthetic vegetation fraction (fPV), and bare soil fraction (fBS); and (3) retrieval of the true CRC fraction at the sowing stage using the formula CRC = fNPV \u002F (1 − fPV). The proposed method was validated and applied regionally using multi-temporal Sentinel-2 MSI and MODIS imagery. Results demonstrate that: (1) the method effectively mitigates moisture interference and achieves high-accuracy fNPV estimation (R2 = 0.84, RMSE = 0.10); (2) the integration of Sentinel-2 and MODIS sensors enables stable estimation and spatio-temporal dynamic analysis of CRC across the North China Plain, with high temporal consistency between images acquired approximately 16 days apart (the majority of agricultural pixels showing absolute differences within ±0.15). This approach provides an efficient and robust technical solution for remote sensing monitoring of CRC fraction in complex agricultural environments and offers a valuable methodological reference for the assessment of conservation tillage practices.","作物残茬覆盖度（CRC）是评估保护性耕作措施、减缓土壤侵蚀以及构建全球碳循环监测模型的关键参数。传统光学遥感指数方法受土壤和作物残茬含水量变化及新生后茬作物干扰的影响显著，导致CRC估算精度低、稳定性差。由于后茬作物的存在，基于线性光谱混合分析提取的非光合植被覆盖度（fNPV）通常低于田间实际CRC值。本研究提出了一种基于线性光谱混合分析的CRC估算新方法。该方法流程主要包括三个部分：（1）利用随机森林回归模型模拟对水分敏感性较低的窄带SINDRI指数，进而构建作物残茬随机森林（CRRF）指数，该指数可由宽带多光谱反射率计算得到；（2）结合CRRF与NDVI指数构建二维三角特征空间，通过线性光谱混合分析同时估算田间fNPV、光合植被覆盖度（fPV）和裸土覆盖度（fBS）；（3）利用公式CRC = fNPV \u002F (1 − fPV)反演播种期真实CRC值。该方法利用多时相Sentinel-2 MSI和MODIS影像进行了验证及区域应用。结果表明：（1）该方法能有效削弱水分干扰，实现高精度fNPV估算（R² = 0.84，RMSE = 0.10）；（2）Sentinel-2与MODIS传感器的联合应用可实现华北平原CRC的稳定估算及时空动态分析，且间隔约16天获取的影像间具有较高时间一致性（绝大多数农业像元的绝对差异在±0.15以内）。该方法为复杂农业环境下CRC覆盖度的遥感监测提供了高效、稳健的技术方案，也为保护性耕作措施评估提供了有价值的方法参考。","International Journal of Remote Sensing","2026-09-02T00:00:00Z",76,{"impact":16,"substance":466,"depth":16,"authority":154,"freshness":121,"relevant":19,"comment":467},20,"提出基于随机森林与线性光谱混合分析的秸秆覆盖度估算新方法，在华北平原多源遥感数据上验证有效，对保护性耕作监测有方法论参考价值。",[469],{"name":462,"url":459},[24,26,27,471,472],"保护性耕作","秸秆覆盖",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[475],{"code":57,"slug":169,"name":170,"path":171,"parent_code":34},{"status":37,"locked":13,"version":38,"method":39,"evidence":477},{"candidates":478,"agricultural_evidence":44},[479,484,488,495,500],{"code":30,"score":480,"supported":44,"title":481,"abstract":482,"tags":483},26,[117,46],[116,117,46],[26],{"code":57,"score":99,"supported":44,"title":485,"abstract":486,"tags":487},[182],[],[27],{"code":489,"score":84,"supported":44,"title":490,"abstract":491,"tags":494},"H2",[],[492,493],"土壤","soil",[],{"code":496,"score":51,"supported":44,"title":497,"abstract":498,"tags":499},"H8",[],[471],[471],{"code":50,"score":19,"supported":13,"title":501,"abstract":502,"tags":503},[],[],[24],[505,506],"保护性耕作 农业遥感 智慧农业 机器学习","保护性耕作 农业遥感","保护性耕作农业遥感智慧农业机器学习-1532","10.1080\u002F01431161.2026.2727176",{"doi":508,"openalex_id":510,"authors":511,"venue":462,"cited_by_count":80,"oa_url":8,"card":528,"direction":142,"ingested_from":81},"W7205007595",[512,515,518,520,523,526],{"name":513,"orcid":514},"Jibo Yue","https:\u002F\u002Forcid.org\u002F0000-0001-9766-5313",{"name":516,"orcid":517},"Ranran Yang","https:\u002F\u002Forcid.org\u002F0000-0003-1937-2042",{"name":519,"orcid":8},"Yinghao Lin",{"name":521,"orcid":522},"Nianxu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-0152-8750",{"name":524,"orcid":525},"Qingjiu Tian","https:\u002F\u002Forcid.org\u002F0000-0003-0986-6479",{"name":527,"orcid":8},"Jia Tian",{"tldr":529,"method":530,"finding":531,"direction":142,"opportunity":532},"提出基于线性光谱混合分析和随机森林的作物残茬覆盖度遥感估算方法，应用于华北平原。","随机森林模拟窄带SINDRI，结合NDVI构建三角特征空间，线性光谱混合分析估算","方法有效缓解水分干扰，fNPV估算R²=0.84，RMSE=0.10，多源影像时间一致性高。","可探索将方法扩展至其他作物类型或区域，或结合高光谱影像提高精度，并研究残茬覆盖度与土壤碳动态的关系。","2026-09-03T23:30:30.109393Z"]