[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2915":3,"related-2915":63},{"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":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":62},2915,"Does hands-on trial experience weaken the negative association between yield-security FoMO and digital nutrient-management technology adoption? Evidence from China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1898755","Excessive fertilizer use remains a major challenge for environmentally sustainable agricultural development in China, particularly in smallholder-based perennial production systems characterized by delayed feedback and production uncertainty. Although portable digital nutrient decision-support technologies may help improve fertilizer-use efficiency, their adoption among farmers remains uneven. Drawing on survey data from 675 kiwifruit farmers in Shaanxi Province, this study applies a multivariate probit model to examine the associations of cognitive evaluations, yield-security fear of missing out (FoMO), and hands-on trial experience with farmers’ adoption of soil testers, chlorophyll meters, and agro-advisory mobile applications. The results show that perceived ease of use and perceived relative advantage are positively associated with adoption, whereas yield-security FoMO is negatively associated with all three adoption outcomes in the baseline models. However, when the item most directly related to individual production-risk concerns is excluded, the negative association remains significant for soil testers and chlorophyll meters but becomes insignificant for agro-advisory mobile applications. Hands-on trial experience was positively associated with the adoption of soil testers and chlorophyll meters, but its association with agro-advisory mobile application adoption was not statistically significant. It was also associated with a weaker negative FoMO–adoption relationship for soil testers and chlorophyll meters, but no statistically significant moderating relationship was observed for agro-advisory mobile applications. These findings highlight the potentially important, but technology-specific, role of structured experiential learning in addressing behavioral barriers and supporting digital nutrient-management transitions in perennial smallholder agriculture.","过量施肥仍然是中国农业环境可持续发展面临的一大挑战，在以小农为基础、具有反馈滞后和生产不确定性的多年生作物生产体系中尤为突出。尽管便携式数字养分决策支持技术可能有助于提高肥料利用效率，但农户对其采用仍不均衡。本研究基于陕西省675户猕猴桃种植户的调查数据，采用多元Probit模型，考察认知评价、产量安全错失恐惧（FoMO）以及亲身试用经历与农户采用土壤测试仪、叶绿素仪和农技咨询移动应用程序之间的关联。结果表明，感知易用性和感知相对优势与采用行为呈正相关，而产量安全错失恐惧在基准模型中与三种技术的采用结果均呈负相关。然而，当剔除与个体生产风险担忧最直接相关的题项后，这种负相关对土壤测试仪和叶绿素仪仍然显著，但对农技咨询移动应用程序则不再显著。亲身试用经历与土壤测试仪和叶绿素仪的采用呈正相关，但其与农技咨询移动应用程序采用之间的关联在统计上不显著。亲身试用经历还弱化了产量安全错失恐惧与土壤测试仪和叶绿素仪采用之间的负相关关系，但对农技咨询移动应用程序未观察到统计上显著的调节关系。这些发现凸显了结构化体验式学习在应对行为障碍、支持多年生小农农业数字养分管理转型方面可能具有重要作用，但这种作用因技术类型而异。",null,"Frontiers in Sustainable Food Systems","2026-09-18T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"基于陕西675户猕猴桃果农调查，揭示产量安全FoMO对数字施肥技术采纳的抑制作用及试用体验的调节效应，对数字农业推广有实证参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农户采纳","猕猴桃","化肥减量","数字农业技术",[33,34],"陕西 猕猴桃 数字施肥","农户 数字营养管理 采纳","陕西猕猴桃数字施肥-2915",0,"10.3389\u002Ffsufs.2026.1898755",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7213534592",[41,44,47,50,53],{"name":42,"orcid":43},"Xia Li","https:\u002F\u002Forcid.org\u002F0000-0001-9933-6679",{"name":45,"orcid":46},"Ahmad Hanis Izani Abdul Hadi","https:\u002F\u002Forcid.org\u002F0000-0001-6512-9938",{"name":48,"orcid":49},"Yeong Sheng Tey","https:\u002F\u002Forcid.org\u002F0000-0002-1252-2485",{"name":51,"orcid":52},"Shaufique Fahmi Sidique","https:\u002F\u002Forcid.org\u002F0000-0002-7545-2816",{"name":54,"orcid":9},"Nolila Mohd Nawi",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"基于陕西675户猕猴桃果农调查，分析产量安全FoMO与亲身体验对数字养分管理技术采纳的影响。","多元Probit模型，675份陕西猕猴桃果农问卷数据。","产量安全FoMO负向关联采纳，亲身体验可弱化该负向关系，但作用因技术类型而异。","数字乡村与农业信息化","可探究不同技术类型下体验式学习干预的设计与效果差异，及其对农户数字养分管理采纳的长期影响。","openalex","2026-09-19T23:30:08.149310Z",{"total":64,"page":22,"page_size":64,"items":65},6,[66,124,148,186,216,244],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":71,"content":9,"source_name":72,"source_url":69,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":81,"tags":83,"search_phrases":87,"slug":90,"view_count":36,"doi":91,"paper":92,"created_at":123},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。","Computers and Electronics in Agriculture","2026-09-15T00:00:00Z",79,{"impact":76,"substance":77,"depth":78,"authority":79,"freshness":21,"relevant":22,"comment":80},16,22,18,14,"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。",[82],{"name":72,"url":69},[27,84,85,29,86],"无人机","农业遥感","叶面积指数",[88,89],"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":91,"openalex_id":93,"authors":94,"venue":72,"cited_by_count":36,"oa_url":69,"card":117,"direction":121,"ingested_from":61},"W7213243341",[95,97,99,101,104,106,108,110,112,115],{"name":96,"orcid":9},"Wenjie Li",{"name":98,"orcid":9},"Hongen Liu",{"name":100,"orcid":9},"Linghuan Ouyang",{"name":102,"orcid":103},"Qian Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7916-1370",{"name":105,"orcid":9},"Tianqi Lv",{"name":107,"orcid":9},"Jiali Li",{"name":109,"orcid":9},"Xintao Lin",{"name":111,"orcid":9},"Chen Yao",{"name":113,"orcid":114},"Yongqiang Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-3246-2800",{"name":116,"orcid":9},"Jianping Qian",{"tldr":118,"method":119,"finding":120,"direction":121,"opportunity":122},"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）。","农业遥感与作物表型","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":125,"title":126,"url":127,"summary":128,"summary_zh":9,"content":129,"source_name":130,"source_url":9,"published_at":131,"category":132,"cover_url":9,"hotness":13,"is_selected":14,"score":133,"score_detail":134,"sources":137,"tags":139,"search_phrases":143,"slug":146,"view_count":36,"doi":9,"paper":9,"created_at":147},2454,"中国农科院资划所构建可解释 AI 模型实现猕猴桃产地精准溯源(AI in Agriculture)","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571168.shtm","中国农科院农业资源与农业区划研究所智慧农业创新团队利用光谱成像技术与多模型组合策略,以中国 6 个地区猕猴桃为对象构建产地溯源模型,并运用可解释性分析方法对最优组合模型进行分层和整体解读。在提升溯源准确率的同时,揭示了光谱数据背后判断产地的内在逻辑。该研究为破解光谱分析中数据分析模型难以解释的难题提供了系统解决思路,在关键波长选择、模型稳定性提升及预测结果可信度增强等方面具有参考价值。成果发表在《Artificial Intelligence in Agriculture》。","[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | 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利用光谱数据判断农产品产地时，现有方法多依赖单一算法，往往只追求预测准确度，却忽略了模型决策逻辑的解释，限制了可靠溯源模型的开发和对判断过程的理解。 科研团队以中国六个地区的猕猴桃为对象，融合光谱成像技术与多模型组合策略构建产地溯源模型，并运用可解释性分析方法对最优组合模型进行分层和整体解读，在提升溯源准确率的同时，揭示了光谱数据背后判断产地的内在逻辑。 该研究为破解光谱分析中数据分析模型难以解释的难题提供了系统解决思路，在关键波长选择、模型稳定性提升及预测结果可信度增强等方面具有参考价值，有助于推动光谱技术在农业生产与品质监管中的实际应用。研究得到国家重点研发计划、中国农业科学院科技创新工程等项目支持。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.05.010 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=571168)\n\n相关新闻 相关论文\n*   1\n*   [基金委发布2个2026年度项目指南](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571167.shtm)\n\n*   2\n*   [2026年度 “澳门青年学者计划”获选结果公布](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F9\u002F571166.shtm)\n\n*   3\n*   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[科学家正尝试把药厂搬到太空](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-41174-1552162.html)\n\n*   [更多>>](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=recommend)","中国科学报 2026-09-09","2026-09-08T16:00:00Z","报道",75,{"impact":78,"substance":135,"depth":19,"authority":79,"freshness":64,"relevant":22,"comment":136},20,"国家级科研团队用可解释AI提升猕猴桃产地溯源准确率并揭示光谱判别逻辑，方法新颖、结论可靠，属智慧农业细分领域实质进展，值得进入每日精选。",[138],{"name":130,"url":127},[27,140,141,29,142],"农业人工智能","农产品溯源","光谱成像",[144,145],"农业人工智能 农产品溯源 光谱成像 智慧农业","农业人工智能 农产品溯源","农业人工智能农产品溯源光谱成像智慧农业-2454","2026-09-15T00:04:22.091088Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":9,"source_name":154,"source_url":151,"published_at":155,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":156,"sources":158,"tags":160,"search_phrases":164,"slug":167,"view_count":36,"doi":168,"paper":169,"created_at":185},2122,"Heterogeneous behaviours towards precision agriculture adoption among Italian winegrowers: insights from latent class analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10448-0","Abstract Purpose Precision Agriculture technologies including satellite data, drones, field robots and automatic guidance, are increasingly promoted as tools to improve sustainability, competitiveness, and environmental efficiency in viticulture. However, the adoption of these practices among Italian winegrowers exhibits significant heterogeneity, influenced by a combination of structural and behavioural factors. This study aims to identify distinct groups of Italian winegrowers based on their intentions to adopt PATs, examining how these intentions relate to four behavioural constructs that capture cognitive, social, and risk-related influences. Method The present study integrates a profile-based perspective by estimating a latent-class generalised structural equation model on 272 Italian winegrowers. The model of class membership is predicated upon a multifaceted conceptualisation encompassing risk perception, risk tolerance, perceived ease of use, and subjective norms and the technology-specific five-year intentions are incorporated as class-specific probit intercepts. Results Three qualitatively distinct profiles emerge: risk-sensitive sceptics, selective pragmatists and usability-oriented high adopters with sharply different adoption patterns across technologies. Participation in rural development schemes is the strongest predictor of membership in the higher-propensity latent classes, namely the selective-pragmatist and usability-oriented high-adopter profiles. In addition, familiarity with PATs, male gender and participation in consortium are positively associated with the higher-propensity class, while age shows a weak negative correlation. Conclusion These results inform the design of extension and policy for specific segments, prioritising risk-mitigation trials for cautious growers, targeted information services for select profiles, and integrated PATs bundles for those with a high propensity.","摘要 目的 精准农业技术（Precision Agriculture Technologies，PATs），包括卫星数据、无人机、田间机器人和自动导航，日益被视为提升葡萄栽培可持续性、竞争力和环境效率的工具。然而，意大利葡萄种植者对上述技术的采纳呈现出显著异质性，受到结构性因素与行为因素的共同影响。本研究旨在基于意大利葡萄种植者采纳PATs的意愿识别不同群体，并考察这些意愿如何与四个行为构念相关联，这四个构念分别捕捉认知、社会和风险相关的影响。方法 本研究整合了基于剖面的视角，对272名意大利葡萄种植者估计了潜在类别广义结构方程模型。类别归属模型建立在涵盖风险感知、风险容忍度、感知易用性和主观规范的多维概念化基础之上，并将技术特定的五年意愿作为类别特定的probit截距纳入模型。结果 研究识别出三种性质不同的剖面：风险敏感型怀疑者、选择性实用主义者和以易用性为导向的高采纳者，其在不同技术上的采纳模式差异显著。参与农村发展计划是归属于较高倾向潜在类别（即选择性实用主义者和以易用性为导向的高采纳者剖面）的最强预测因素。此外，对PATs的熟悉程度、男性性别和参与合作社与较高倾向类别呈正相关，而年龄则表现出较弱的负相关。结论 上述结果为针对特定群体的推广和政策设计提供了依据，应优先为谨慎型种植者开展风险缓解试验，为特定剖面提供有针对性的信息服务，并为高倾向群体提供整合的PATs技术组合。","Precision Agriculture","2026-09-10T00:00:00Z",{"impact":76,"substance":77,"depth":78,"authority":79,"freshness":21,"relevant":22,"comment":157},"基于272户意大利葡萄种植者的潜类别分析，揭示精准农业采纳的异质性行为分群，对农户分类推广与政策设计有实质参考价值。",[159],{"name":154,"url":151},[27,28,161,162,163],"精准农业","遥感监测","葡萄种植",[165,166],"农户采纳 智慧农业 精准农业 葡萄种植","农户采纳 智慧农业","农户采纳智慧农业精准农业葡萄种植-2122","10.1007\u002Fs11119-026-10448-0",{"doi":168,"openalex_id":170,"authors":171,"venue":154,"cited_by_count":36,"oa_url":151,"card":180,"direction":59,"ingested_from":61},"W7212151394",[172,174,177],{"name":173,"orcid":9},"Adriano Biondo",{"name":175,"orcid":176},"Antonino Galati","https:\u002F\u002Forcid.org\u002F0000-0003-0753-2934",{"name":178,"orcid":179},"Francesco Caracciolo","https:\u002F\u002Forcid.org\u002F0000-0001-9430-7529",{"tldr":181,"method":182,"finding":183,"direction":59,"opportunity":184},"基于272名意大利葡萄种植者，用潜类别分析识别精准农业技术采纳意向的异质性群体。","潜类别广义结构方程模型，纳入风险感知、风险容忍、易用性和主观规范。","分出风险敏感怀疑者、选择性实用主义者和易用性高采纳者三类，参与农村发展计划是最强预测因素。","可针对不同农户群体设计差异化推广策略，并研究政策参与如何通过行为路径影响技术采纳。","2026-09-11T23:30:02.943184Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":9,"content":9,"source_name":191,"source_url":9,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":196,"tags":198,"search_phrases":202,"slug":205,"view_count":36,"doi":9,"paper":206,"created_at":215},3002,"改进生物神经网络的农业播种机全覆盖路径规划","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1968","江苏大学魏军等提出一种考虑播种与非播种运动模式切换机制的改进生物神经网络（BNN）方法，基于周围环境条件将下一节点状态分类为播种、封闭或转移节点。在BNN景观引导下机器沿平行直线路径继续播种操作；检测到封闭节点时切换至非播种模式并使用深度优先搜索算法搜索潜在封闭区域；检测到转移节点时同样切换非播种模式搜索合理的新目标节点。仿真表明该方法实现播种操作的完全覆盖同时避免重复遍历已播种区域。","MDPI Agriculture 16(18):1968","2026-09-14T00:00:00Z",69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":64,"relevant":22,"comment":195},"提出改进生物神经网络的全覆盖路径规划方法，方法新颖、结论可靠，但属细分领域学术进展，公共影响有限。",[197],{"name":191,"url":189},[27,140,199,200,201],"智能农机","路径规划","播种机",[203,204],"江苏大学 播种机 全覆盖路径规划","生物神经网络 播种机 路径规划","江苏大学播种机全覆盖路径规划-3002",{"doi":9,"openalex_id":9,"authors":207,"venue":9,"cited_by_count":36,"oa_url":9,"card":208,"direction":212,"ingested_from":214},[],{"tldr":209,"method":210,"finding":211,"direction":212,"opportunity":213},"提出改进生物神经网络，实现农业播种机全覆盖路径规划并避免重复播种。","改进BNN结合节点分类与深度优先搜索，仿真验证。","方法实现播种完全覆盖，同时避免重复遍历已播种区域。","农业人工智能与决策模型","可结合真实农田地形与多机协同，验证动态环境下的路径规划鲁棒性。","agent","2026-09-20T00:03:08.288198Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":9,"content":9,"source_name":221,"source_url":9,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":222,"score_detail":223,"sources":226,"tags":228,"search_phrases":232,"slug":235,"view_count":36,"doi":9,"paper":236,"created_at":243},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。","MDPI Agronomy 16(18):1811",74,{"impact":224,"substance":77,"depth":78,"authority":20,"freshness":64,"relevant":22,"comment":225},15,"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。",[227],{"name":221,"url":219},[27,161,229,230,231],"遥感","作物表型","春小麦",[233,234],"天津师范大学 春小麦 多光谱","UAV 多光谱 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