[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"daily-2026-10-03":3},{"date":4,"title":5,"highlights":6,"content":13,"items":14},"2026-10-03","农业农村日报：AI赋能杂草识别与土壤管理前沿",[7,8,9,10,11,12],"WeedNet提出基于基础模型的全局到局部AI方法，实现杂草物种早期精准识别与分类，助力自动化田间管理。","可穿戴微流控平台通过植物吐水液滴无创监测生物标志物，为作物生理状态提供外部可读窗口。","中国数字乡村建设政策被多项研究证实可提升粮食生产安全，并推动农民专业合作社数字化升级与转型。","农村人口收缩与耕地非粮化关系获机器学习双因果推断验证，为保障粮食安全提供新证据。","印度小杂粮综述揭示其营养安全、气候韧性与可持续生计价值；AI土壤管理综述强调气候韧性提升路径。","RTAS辐射传输模型与UGV多RGBD SLAM框架分别服务条带间作和玉米高通量原位3D表型，支撑智慧育种。","本期聚焦AI与数字技术在杂草识别、土壤管理、作物表型及合作社转型中的应用，兼论农村数字化对粮食安全的影响、人口收缩与耕地非粮化因果，以及小杂粮气候韧性与循环生物经济机制设计。\n\n---\n*本日报内容整理自公开来源，学术论文元数据来自 OpenAlex 等开放接口；外文资料已译为中文，翻译与摘要仅供参考；引用与决策请以官方原文与正式出版物为准。*",[15,145,234,315,379,441,573,638,737,802,887,963,1038,1105,1173],{"id":16,"title":17,"url":18,"summary":19,"summary_zh":20,"content":21,"source_name":22,"source_url":18,"published_at":23,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":27,"score_detail":28,"sources":36,"tags":38,"primary_topic":44,"secondary_topics":50,"classification":51,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":144},4039,"WeedNet: a foundation model-based global-to-local AI approach for weed species identification and classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-026-78044-4","Early weed identification is crucial for effective management and control, and researchers, agronomists, and technology developers are increasingly interested in automating this process using computer vision and artificial intelligence; however, limited expert-verified data and variable morphological features have hindered the development of AI-based weed identification models. To address these issues, we present WeedNet, a global-scale weed identification model that can recognize an extensive set of weed species. WeedNet is an end-to-end weed identification pipeline that uses self-supervised learning, fine-tuning, and enhanced trustworthiness strategies. WeedNet achieved 91.02% accuracy across 1,593 weed species, with 41% of species achieving 100% accuracy. Using a fine-tuning approach, the local Iowa WeedNet model achieved 97.38% overall accuracy for 84 Iowa weeds. Testing across intra-species dissimilarity and inter-species similarity suggests that diversity in the images collected, spanning all the growth stages and distinguishable plant characteristics, is crucial in driving model performance. The global WeedNet model serves as a foundation, and the global-to-local strategy enables targeted fine-tuning to improve performance in regional weed communities. Additional validation of drone- and ground-rover-based images highlights WeedNet’s potential for integration into robotic platforms. Furthermore, integration with artificial intelligence for conversational use provides intelligent agricultural and ecological conservation consulting tools for farmers, researchers, and government agencies across diverse landscapes. Weeds consistently threaten crop yields worldwide, making early and accurate identification essential for effective management. Trained on 14 million images representing 1,593 weed species, WeedNet achieved 91% global accuracy and, through global-to-local fine-tuning, reached 97% accuracy for Iowa weeds.","早期杂草识别对有效管理和控制至关重要，研究人员、农艺学家和技术开发者对利用计算机视觉和人工智能实现该过程自动化的兴趣日益浓厚；然而，专家验证数据有限以及形态特征多变，阻碍了基于人工智能的杂草识别模型的发展。为解决这些问题，我们提出了WeedNet，一个能够识别大量杂草物种的全球尺度杂草识别模型。WeedNet是一个端到端的杂草识别流程，采用自监督学习、微调以及增强可信度策略。WeedNet在1,593种杂草上实现了91.02%的准确率，其中41%的物种达到100%准确率。采用微调方法，爱荷华本地WeedNet模型对84种爱荷华杂草实现了97.38%的总体准确率。对种内差异性和种间相似性的测试表明，所采集图像中涵盖所有生长阶段和可区分植物特征的多样性，对驱动模型性能至关重要。全球WeedNet模型作为基础，全球到本地策略能够实现有针对性的微调，以提升区域杂草群落中的性能。对无人机和地面巡游机器人图像进行的额外验证，凸显了WeedNet集成到机器人平台中的潜力。此外，与人工智能集成用于对话式使用，可为农民、研究人员和政府机构在不同景观中提供智能农业和生态保护咨询工具。杂草持续威胁全球作物产量，因此早期准确识别对有效管理至关重要。WeedNet在代表1,593种杂草的1,400万张图像上训练，实现了91%的全球准确率，并通过全球到本地微调，对爱荷华杂草达到97%的准确率。",null,"Nature Communications","2026-09-30T00:00:00Z","论文",10,false,89,{"impact":29,"substance":30,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":35},24,23,19,15,8,1,"基于1400万图像、覆盖1593种杂草的全球基础模型，全局到局部微调思路清晰，对智慧农业与农业机器人有实质参考价值。",[37],{"name":22,"url":18},[39,40,41,42,43],"智慧农业","农业人工智能","计算机视觉","农业机器人","杂草识别",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":55},"classified","2026-10-22.2","rules",{"candidates":56,"agricultural_evidence":59},[57,65,70,75],{"code":45,"score":58,"supported":59,"title":60,"abstract":61,"tags":64},11,true,[],[41,62,63],"artificial intelligence","computer vision",[40,41],{"code":66,"score":34,"supported":26,"title":67,"abstract":68,"tags":69},"F4",[],[],[43],{"code":71,"score":34,"supported":26,"title":72,"abstract":73,"tags":74},"F6",[],[],[42],{"code":76,"score":34,"supported":26,"title":77,"abstract":78,"tags":79},"I1",[],[],[39],[81,82],"WeedNet 杂草识别","Nature Communications 杂草识别模型","WeedNet杂草识别-4039",3,"10.1038\u002Fs41467-026-78044-4",{"doi":85,"openalex_id":87,"authors":88,"venue":22,"cited_by_count":136,"oa_url":18,"card":137,"direction":141,"ingested_from":143},"W7214939964",[89,92,95,98,101,103,105,108,111,114,116,119,120,122,125,127,130,133,135],{"name":90,"orcid":91},"Yanben Shen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-3198",{"name":93,"orcid":94},"Timilehin T. Ayanlade","https:\u002F\u002Forcid.org\u002F0000-0003-4792-5337",{"name":96,"orcid":97},"Venkata Naresh Boddepalli","https:\u002F\u002Forcid.org\u002F0009-0008-0216-9156",{"name":99,"orcid":100},"Mojdeh Saadati","https:\u002F\u002Forcid.org\u002F0000-0002-9092-8464",{"name":102,"orcid":21},"Ashlyn Rairdin",{"name":104,"orcid":21},"Zi Kang Deng",{"name":106,"orcid":107},"Muhammad Arshad","https:\u002F\u002Forcid.org\u002F0000-0003-1574-7097",{"name":109,"orcid":110},"Baskar Ganapathysubramanian","https:\u002F\u002Forcid.org\u002F0000-0003-4042-066X",{"name":112,"orcid":113},"Aditya Balu","https:\u002F\u002Forcid.org\u002F0000-0003-2005-2548",{"name":115,"orcid":21},"Daren Mueller",{"name":117,"orcid":118},"Asheesh Kumar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-7522-037X",{"name":117,"orcid":118},{"name":121,"orcid":21},"Wesley Everman",{"name":123,"orcid":124},"Nirav Merchant","https:\u002F\u002Forcid.org\u002F0000-0002-1988-8180",{"name":109,"orcid":126},"https:\u002F\u002Forcid.org\u002F0000-0002-8931-4852",{"name":128,"orcid":129},"Meaghan J. B. Anderson","https:\u002F\u002Forcid.org\u002F0009-0001-5904-1574",{"name":131,"orcid":132},"Soumik Sarkar","https:\u002F\u002Forcid.org\u002F0000-0002-6775-9199",{"name":134,"orcid":21},"Arti Singh",{"name":134,"orcid":21},0,{"tldr":138,"method":139,"finding":140,"direction":141,"opportunity":142},"提出WeedNet基础模型，实现全球杂草物种识别与区域微调。","自监督学习、微调与可信策略，基于1400万张图像训练。","全球1593种杂草识别准确率91%，爱荷华84种微调后达97%。","农业人工智能与决策模型","可探索跨区域杂草基础模型微调与无人机\u002F机器人平台集成。","openalex","2026-10-02T23:30:41.979601Z",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":21,"source_name":22,"source_url":148,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":27,"score_detail":152,"sources":154,"tags":156,"primary_topic":161,"secondary_topics":165,"classification":166,"search_phrases":174,"slug":177,"view_count":34,"doi":178,"paper":179,"created_at":233},4021,"A multiplexed wearable microfluidic platform for non-invasive sweat-inspired monitoring of plant guttation biomarkers","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-026-77955-6","Plant guttation fluid provides a chemically informative readout of internal plant status, offering an externally accessible window into plant physiological processes. However, its minute volume, intermittent availability, and susceptibility to evaporation limit its use for continuous biochemical monitoring. Here we introduce PlantIonics, an integrated on-leaf microfluidic electrochemical platform for real-time, non-invasive monitoring of guttation biomarkers and leaf microenvironment. A bioinspired wedge-shaped microchannel with asymmetric wettability achieves rapid (0.6 s) and unidirectional self-driven fluid collection, providing a continuously refreshed sensing interface for dynamic guttation profiling. Combined with a multichannel sensor array and a compact wireless flexible printed circuit board, PlantIonics simultaneously measures guttation-fluid biomarkers (potassium, sodium, glucose) and local leaf microenvironment. Multi-day on-plant studies demonstrate stress-responsive guttation fluctuations that correlate with root-level ionic perturbations and light modulation, demonstrating the platform’s capability to monitor dynamic physiological regulation. PlantIonics establishes a sweat-inspired analytical framework for non-invasive plant monitoring, offering opportunities for early stress diagnostics, field phenotyping, and precision agriculture. Plant sensors are emerging as key tools for precision agriculture, yet continuous monitoring of biomarkers in plant biofluids remains underdeveloped. Here, the authors introduce PlantIonics, a wearable microfluidic electrochemical platform for multiplexed, in situ analysis of guttation fluid. The system enables continuous, noninvasive monitoring of stress-associated plant physiological and metabolic responses under changing environmental conditions.","植物吐水液提供了一种化学信息丰富的植物内部状态读数，为了解植物生理过程提供了一个可从外部获取的窗口。然而，其微量体积、间歇性可用性以及易蒸发的特性限制了其在连续生化监测中的应用。在此，我们介绍PlantIonics，一种集成于叶片上的微流控电化学平台，用于实时、非侵入式监测吐水生物标志物和叶片微环境。一种具有不对称润湿性的仿生楔形微通道实现了快速（0.6秒）单向自驱动流体收集，为动态吐水分析提供了持续更新的传感界面。结合多通道传感器阵列和紧凑型无线柔性印刷电路板，PlantIonics可同时测量吐水液生物标志物（钾、钠、葡萄糖）和局部叶片微环境。多日活体植物研究证明了应激响应性吐水波动与根部离子扰动和光照调节相关，展示了该平台监测动态生理调控的能力。PlantIonics建立了一个受汗液分析启发的非侵入式植物监测分析框架，为早期胁迫诊断、田间表型分析和精准农业提供了机遇。植物传感器正成为精准农业的关键工具，但植物生物流体中生物标志物的连续监测仍发展不足。在此，作者介绍了PlantIonics，一种可穿戴微流控电化学平台，用于吐水液的多重原位分析。该系统能够在不断变化的环境条件下连续、非侵入式监测与胁迫相关的植物生理和代谢响应。","2026-10-01T00:00:00Z",{"impact":29,"substance":30,"depth":31,"authority":32,"freshness":33,"relevant":34,"comment":153},"Nature Communications 刊发的植物可穿戴微流控电化学平台，实现吐水液钾钠糖与叶微环境连续无创监测，方法新颖、结论可靠，对精准农业与作物早期胁迫诊断具产业级参考价值。",[155],{"name":22,"url":148},[39,157,158,159,160],"精准农业","植物可穿戴传感器","微流控传感","植物胁迫监测",{"code":76,"slug":162,"name":163,"path":164,"parent_code":49},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":167},{"candidates":168,"agricultural_evidence":59},[169],{"code":76,"score":33,"supported":59,"title":170,"abstract":171,"tags":173},[],[157,172],"precision agriculture",[39,157],[175,176],"PlantIonics 植物传感器","植物吐水 微流控监测","PlantIonics植物传感器-4021","10.1038\u002Fs41467-026-77955-6",{"doi":178,"openalex_id":180,"authors":181,"venue":22,"cited_by_count":136,"oa_url":148,"card":226,"direction":232,"ingested_from":143},"W7215080854",[182,185,187,190,193,195,198,200,202,205,208,210,212,214,217,220,223],{"name":183,"orcid":184},"Bing Xue","https:\u002F\u002Forcid.org\u002F0000-0001-8854-2151",{"name":186,"orcid":21},"Ziyang Huang",{"name":188,"orcid":189},"Zhiyuan Li","https:\u002F\u002Forcid.org\u002F0009-0002-6892-8086",{"name":191,"orcid":192},"Xue Sun","https:\u002F\u002Forcid.org\u002F0000-0002-7971-4051",{"name":194,"orcid":21},"Zhefu Shen",{"name":196,"orcid":197},"Junyi Luo","https:\u002F\u002Forcid.org\u002F0000-0002-5102-389X",{"name":199,"orcid":21},"Kejing Ren",{"name":201,"orcid":21},"Wei Zhang",{"name":203,"orcid":204},"Ruochen Yang","https:\u002F\u002Forcid.org\u002F0009-0002-4847-3491",{"name":206,"orcid":207},"Xiao Xiao","https:\u002F\u002Forcid.org\u002F0000-0002-7861-5596",{"name":209,"orcid":21},"Xiaoqian Su",{"name":211,"orcid":21},"Shuoyan Liu",{"name":213,"orcid":21},"Anxu Ge",{"name":215,"orcid":216},"Jinghua Li","https:\u002F\u002Forcid.org\u002F0000-0001-8985-566X",{"name":218,"orcid":219},"R.D.I.G. Dharmasena","https:\u002F\u002Forcid.org\u002F0000-0002-7959-2840",{"name":221,"orcid":222},"Tedrick Thomas Salim Lew","https:\u002F\u002Forcid.org\u002F0000-0002-4815-9921",{"name":224,"orcid":225},"Changsheng Wu","https:\u002F\u002Forcid.org\u002F0000-0002-0921-6356",{"tldr":227,"method":228,"finding":229,"direction":230,"opportunity":231},"开发可穿戴微流控电化学平台PlantIonics，实时无创监测植物吐水液生物标志物。","仿生楔形微通道自驱动集液，多通道传感器阵列与柔性无线电路板。","实现钾、钠、葡萄糖及叶微环境连续监测，吐水波动与根部离子和光照相关。","其他","可延伸至田间多作物长期表型监测，结合AI实现早期胁迫诊断与精准灌溉决策。","农业遥感与作物表型","2026-10-02T23:30:21.815665Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":21,"source_name":240,"source_url":237,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":241,"score_detail":242,"sources":248,"tags":250,"primary_topic":256,"secondary_topics":261,"classification":262,"search_phrases":289,"slug":292,"view_count":293,"doi":294,"paper":295,"created_at":314},4003,"Does rural digitalization improve food production security? Evidence from China’s digital village construction policy","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1914989","Introduction Food production security (FPS) remains a core challenge for developing economies. Although rural digitalization serves as a fundamental strategy to reshape agricultural systems in developing countries, its impact on food supply resilience remains underexplored. Methods This study investigates the impact of rural digitalization on regional FPS by exploiting China’s Digital Village Construction policy as a quasi-natural experiment. We employ the DID model on a panel dataset comprising 334 prefecture-level cities from 2015 to 2024. Results The empirical results demonstrate that rural digitalization significantly improves regional FPS. Specifically, the digital intervention increases the FPS of treated cities by an average of 8.2 percent. Regarding the mechanisms, we find that rural digitalization enhances FPS by facilitating smallholders’ food production viability and promoting food-oriented land reallocation. Furthermore, heterogeneity analyses reveal that the impact is more pronounced in regions equipped with superior agricultural socialized services, advanced agricultural industry structures, and major food producing area designations. Discussion These findings demonstrate that comprehensive rural digitalization strengthens the resilience of food production systems in developing economies and offer guidance for other Global South countries pursuing similar rural development paradigms.","引言 粮食生产安全（Food Production Security, FPS）仍是发展中经济体面临的核心挑战。尽管乡村数字化是发展中国家重塑农业体系的基础性战略，但其对粮食供给韧性的影响仍缺乏深入探讨。方法 本研究以中国数字乡村建设政策作为准自然实验，考察乡村数字化对区域粮食生产安全的影响。我们基于2015年至2024年334个地级市的面板数据集，采用双重差分（DID）模型进行实证分析。结果 实证结果表明，乡村数字化显著提升了区域粮食生产安全。具体而言，数字干预使处理组城市的粮食生产安全平均提高8.2%。在机制方面，我们发现乡村数字化通过促进小农户的粮食生产可行性和推动粮食导向的土地再配置来增强粮食生产安全。此外，异质性分析显示，在农业社会化服务较优、农业产业结构较先进以及被划定为粮食主产区的地区，该影响更为显著。讨论 这些发现表明，全面的乡村数字化增强了发展中经济体粮食生产体系的韧性，并为其他全球南方国家追求类似乡村发展范式提供了借鉴。","Frontiers in Sustainable Food Systems",86,{"impact":29,"substance":243,"depth":244,"authority":245,"freshness":246,"relevant":34,"comment":247},22,18,13,9,"基于中国数字乡村政策准自然实验，用DID方法量化农村数字化对粮食生产安全的提升效应，数据规模大、结论可靠，对数字乡村与粮食安全主题有较高参考价值。",[249],{"name":240,"url":237},[251,252,253,254,255],"数字乡村","粮食安全","发展中国家","农业社会化服务","准自然实验",{"code":257,"slug":258,"name":259,"path":260,"parent_code":49},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":263},{"candidates":264,"agricultural_evidence":59},[265,270,278,284],{"code":257,"score":32,"supported":59,"title":266,"abstract":268,"tags":269},[267],"digital village",[251,267],[251],{"code":271,"score":272,"supported":59,"title":273,"abstract":274,"tags":277},"G2",7,[],[275,276],"社会化服务","小农户",[254],{"code":279,"score":84,"supported":59,"title":280,"abstract":281,"tags":283},"G",[],[282],"rural development",[],{"code":285,"score":34,"supported":26,"title":286,"abstract":287,"tags":288},"A8",[],[],[252],[290,291],"数字乡村建设 粮食生产安全","农村数字化 粮食安全","数字乡村建设粮食生产安全-4003",2,"10.3389\u002Ffsufs.2026.1914989",{"doi":294,"openalex_id":296,"authors":297,"venue":240,"cited_by_count":136,"oa_url":237,"card":308,"direction":312,"ingested_from":143},"W7215123040",[298,301,303,306],{"name":299,"orcid":300},"Meng Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-2049-9147",{"name":302,"orcid":21},"Jianhua Wang",{"name":304,"orcid":305},"Jiaqing Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-4702-9495",{"name":307,"orcid":21},"Zixuan Ai",{"tldr":309,"method":310,"finding":311,"direction":312,"opportunity":313},"以中国数字乡村建设政策为准自然实验，评估农村数字化对区域粮食生产安全的影响。","基于334个地级市2015—2024年面板数据，采用双重差分（DID）模型。","农村数字化使处理组城市粮食生产安全平均提升8.2%，通过小农户生产可行性与耕地再配置实现。","数字乡村与农业信息化","可探究数字乡村政策对粮食安全的长期动态效应及社会化服务、产业结构的门槛机制。","2026-10-02T23:30:10.524948Z",{"id":316,"title":317,"url":318,"summary":319,"summary_zh":320,"content":21,"source_name":240,"source_url":318,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":321,"score_detail":322,"sources":325,"tags":327,"primary_topic":332,"secondary_topics":333,"classification":334,"search_phrases":360,"slug":363,"view_count":364,"doi":365,"paper":366,"created_at":378},4006,"The impact of rural population shrinkage on cultivated land non-grain conversion—dual causal inference based on machine learning","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1898293","Against the backdrop of rapid advances in new urbanization and continued rural population contraction, the expansion of cropland non-grain use poses a potential challenge to national food security. Based on 3,598 observations from 257 prefecture-level cities in China over the 2011–2024 period, this study introduces a double machine learning approach and combines two-way fixed-effects models, threshold models, and spatial Durbin models to systematically examine the mechanisms through which rural population contraction affects cropland non-grain use. The results show: (1) rural population contraction significantly raises the degree of cropland non-grain use: for every one-unit increase in rural population contraction, cropland non-grain use rises by 0.089 units. (2) The relationship exhibits nonlinear characteristics, with per-mu grain returns serving as the threshold variable. As per-mu grain returns increase, the effect follows a pattern of “strong promotion–weak promotion–significant inhibition.” (3) Heterogeneity results show that the promoting effect of rural population decline on cropland non-grainization is more pronounced in eastern regions and major grain-marketing areas. (4) Spatial econometric analysis confirms that rural population decline exhibits significant positive spatial spillover effects, with the magnitude of the indirect spillover effect substantially exceeding that of the local direct effect. These findings are expected to provide empirical evidence and decision-making references for advancing differentiated and tiered governance of cropland non-grainization and strengthening food security.","在新型城镇化快速推进与乡村人口持续收缩的背景下，耕地非粮化扩张对国家粮食安全构成潜在挑战。本研究基于2011—2024年中国257个地级市的3598个观测样本，引入双重机器学习方法，并结合双向固定效应模型、门槛模型和空间杜宾模型，系统考察乡村人口收缩影响耕地非粮化的作用机制。结果表明：(1)乡村人口收缩显著提高耕地非粮化程度：乡村人口收缩每增加一个单位，耕地非粮化程度上升0.089个单位。(2)二者关系呈现非线性特征，以亩均粮食收益为门槛变量，随着亩均粮食收益提高，其作用表现为“强促进—弱促进—显著抑制”的演变规律。(3)异质性结果显示，乡村人口减少对耕地非粮化的促进效应在东部地区与粮食主销区更为明显。(4)空间计量分析证实，乡村人口减少具有显著的正向空间溢出效应，其间接溢出效应强度明显超过本地直接效应。上述发现可为推进耕地非粮化差异化、分层化治理与强化粮食安全保障提供经验证据与决策参考。",85,{"impact":243,"substance":30,"depth":244,"authority":323,"freshness":33,"relevant":34,"comment":324},14,"基于257个地级市面板数据与双重机器学习因果推断，量化乡村人口收缩对耕地非粮化的非线性与空间溢出影响，方法新颖、结论对差异化耕地治理有参考价值。",[326],{"name":240,"url":318},[252,328,329,330,331],"机器学习","空间溢出效应","耕地非粮化","乡村人口收缩",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[],{"status":52,"locked":26,"version":53,"method":54,"evidence":335},{"candidates":336,"agricultural_evidence":59},[337,342,348,353],{"code":45,"score":32,"supported":59,"title":338,"abstract":340,"tags":341},[339],"machine learning",[328,339],[328],{"code":343,"score":33,"supported":59,"title":344,"abstract":346,"tags":347},"I7",[345],"causal inference",[],[],{"code":285,"score":272,"supported":59,"title":349,"abstract":350,"tags":352},[],[252,351],"food security",[252],{"code":354,"score":355,"supported":59,"title":356,"abstract":357,"tags":359},"I4",4,[],[358],"空间计量",[329],[361,362],"耕地非粮化 乡村人口收缩","双机器学习 粮食安全","耕地非粮化乡村人口收缩-4006",6,"10.3389\u002Ffsufs.2026.1898293",{"doi":365,"openalex_id":367,"authors":368,"venue":240,"cited_by_count":136,"oa_url":318,"card":373,"direction":312,"ingested_from":143},"W7215130187",[369,371],{"name":370,"orcid":21},"Wuwei Zhang",{"name":372,"orcid":21},"Shuo Zang",{"tldr":374,"method":375,"finding":376,"direction":312,"opportunity":377},"基于中国257个地级市数据，用双机器学习等方法研究农村人口收缩对耕地非粮化的影响。","双机器学习结合双向固定效应、门槛模型和空间杜宾模型，2011-2024年3598","农村人口收缩显著加剧耕地非粮化，存在以亩均粮食收益为门槛的非线性关系及正向空间溢出。","可探究数字技术或农业信息化能否调节人口收缩对非粮化的影响，并关注空间溢出的跨区治理机制。","2026-10-02T23:30:10.758271Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":21,"source_name":385,"source_url":382,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":386,"score_detail":387,"sources":389,"tags":391,"primary_topic":396,"secondary_topics":397,"classification":398,"search_phrases":422,"slug":425,"view_count":84,"doi":426,"paper":427,"created_at":440},4033,"The Upgrading and Transformation of Farmer Professional Cooperative in the Digital Age: Insights from China Digital Village Policy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feconomies14100435","Farmer professional cooperatives are important vehicles for agricultural modernisation. In the digital age, the development of cooperatives has followed two typical pathways: upgrading in scale and transformation in business operations. Existing research has focused on the impact of digitalisation on the former, while paying less attention to the issue of structural transformation within cooperatives. To investigate this issue, this study constructs a difference-in-differences model based on a sample of data from Chinese farmer professional cooperatives. By leveraging the quasi-natural experiment of the digital village policy to construct variables, this study examines the impact of rural digitalisation on cooperative upgrading and transformation and analyses the mechanisms driving the divergence of cooperative development pathways. The study indicates that: (1) The China Digital Village Policy has had a significant positive impact on both the transformation and upgrading of farmer professional cooperatives. (2) Conditional on observable variables, the China Digital Village Policy is positively associated with cooperative upgrading and transformation, with patterns consistent with potential pathways through information channel capability and technological innovation capability. (3) Under the same observable-variable constraints, market expansion capability and capital acquisition capability show conditional association patterns that are significantly positively associated with cooperative upgrading. For cooperative transformation, however, they show conditional suppression patterns. This indicates that agricultural digitalisation is closely linked to cooperative upgrading and transformation. The differentiated empirical patterns of digitalisation along the dimensions of information channels, technological innovation, market expansion and capital acquisition provide empirical evidence for understanding the pathways of cooperative differentiation.","农民专业合作社是农业现代化的重要载体。在数字时代，合作社发展遵循了两条典型路径：规模升级与经营转型。已有研究主要关注数字化对前者的影响，而对合作社结构转型问题关注不足。为探究这一问题，本研究基于中国农民专业合作社数据样本构建了双重差分模型。通过利用数字乡村政策的准自然实验构建变量，本研究考察了乡村数字化对合作社升级与转型的影响，并分析了驱动合作社发展路径分化的机制。研究表明：(1) 中国数字乡村政策对农民专业合作社的转型与升级均产生了显著正向影响。(2) 在可观测变量条件下，中国数字乡村政策与合作社升级和转型呈正相关，其模式与通过信息渠道能力和技术创新能力的潜在路径相一致。(3) 在相同的可观测变量约束下，市场拓展能力和资本获取能力与合作社升级呈现显著正相关的条件关联模式；然而对于合作社转型，它们则呈现条件抑制模式。这表明农业数字化与合作社升级和转型密切相关。数字化在信息渠道、技术创新、市场拓展和资本获取等维度上的差异化实证模式，为理解合作社分化路径提供了经验证据。","Economies",84,{"impact":243,"substance":243,"depth":244,"authority":245,"freshness":246,"relevant":34,"comment":388},"基于中国数字乡村政策准自然实验的双重差分研究，方法规范、结论有新意，对数字乡村与合作社发展路径具参考价值。",[390],{"name":385,"url":382},[251,392,393,394,395],"农业数字化","农业现代化","农民合作社","合作社转型",{"code":257,"slug":258,"name":259,"path":260,"parent_code":49},[],{"status":52,"locked":26,"version":53,"method":54,"evidence":399},{"candidates":400,"agricultural_evidence":59},[401,405,410,414,418],{"code":257,"score":32,"supported":59,"title":402,"abstract":403,"tags":404},[267],[251,267],[251],{"code":406,"score":355,"supported":59,"title":407,"abstract":408,"tags":409},"J2",[],[393],[393],{"code":271,"score":34,"supported":26,"title":411,"abstract":412,"tags":413},[],[],[394],{"code":49,"score":34,"supported":26,"title":415,"abstract":416,"tags":417},[],[],[392],{"code":76,"score":34,"supported":26,"title":419,"abstract":420,"tags":421},[],[],[392],[423,424],"中国数字乡村政策 农民专业合作社","数字乡村 合作社 转型升级","中国数字乡村政策农民专业合作社-4033","10.3390\u002Feconomies14100435",{"doi":426,"openalex_id":428,"authors":429,"venue":385,"cited_by_count":136,"oa_url":382,"card":435,"direction":312,"ingested_from":143},"W7215037926",[430,432],{"name":431,"orcid":21},"Yingying Zhang",{"name":433,"orcid":434},"Yaodong Zhou","https:\u002F\u002Forcid.org\u002F0000-0002-7573-2850",{"tldr":436,"method":437,"finding":438,"direction":312,"opportunity":439},"基于中国数字乡村政策准自然实验，用双重差分法检验数字化对农民合作社升级与转型的影响。","双重差分模型，中国农民专业合作社样本，数字乡村政策准自然实验。","数字乡村政策显著促进合作社升级与转型，信息渠道与技术创新为潜在路径，市场与资本能力对升级促进、对转型","可深入探究市场扩张与资本获取能力为何抑制合作社业务转型，及其组织与制度边界条件。","2026-10-02T23:30:30.775133Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":21,"source_name":240,"source_url":444,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":386,"score_detail":447,"sources":449,"tags":451,"primary_topic":457,"secondary_topics":463,"classification":464,"search_phrases":529,"slug":532,"view_count":34,"doi":533,"paper":534,"created_at":572},4002,"Small millets in India: a review of nutritional security, climate resilience and sustainable livelihoods","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1919531","Small millets ( Setaria italica, Panicum sumatrense, Paspalum scrobiculatum, Echinochloa frumentacea, Panicum miliaceum and Brachiaria ramosa ) are emerging as climate-resilient, nutrient-dense crops with considerable potential to address food insecurity, malnutrition, environmental degradation and livelihood vulnerabilities. Although these crops have long been cultivated under diverse agroecological conditions, their production and consumption declined following the dominance of rice and wheat-based production systems during the Green Revolution. This systematic literature review synthesizes current evidence on the nutritional composition, climate resilience, socio-economic importance, value addition, market opportunities, institutional support and policy interventions related to small millets in India. The review followed the PRISMA 2020 guidelines for systematic literature identification, screening and study selection. Literature published up to June 2026 was systematically retrieved from Scopus, Web of Science Core Collection, ScienceDirect, SpringerLink, CAB Abstracts, AGRICOLA and Google Scholar, together with relevant reports from national and international organizations. After applying predefined inclusion and exclusion criteria, 150 studies were included in the qualitative synthesis. The evidence indicates that small millets are rich sources of dietary fibre, quality protein, essential minerals, vitamins and bioactive compounds such as polyphenols and flavonoids, contributing to nutritional security and reducing the risk of non-communicable diseases. Their tolerance to drought, heat stress, poor soil fertility and low-input production systems makes them highly suitable for climate-smart and sustainable agriculture. Beyond their agronomic and nutritional advantages, small millets support the livelihoods of tribal and marginal farming communities, strengthen indigenous knowledge systems and enhance household food security. The review further highlights the significant role of women in millet cultivation, processing, value addition and marketing, as well as the contributions of self-help groups and farmer organizations to rural development and income generation. Advances in processing technologies, product diversification, entrepreneurship and export opportunities have enhanced the economic potential of the millet sector. National and international initiatives, including the International Year of Millets 2023, millet missions, nutrition-sensitive programmes and institutional interventions, have further accelerated millet promotion. However, challenges such as low productivity, inadequate processing infrastructure, weak market linkages, limited consumer awareness and policy implementation gaps continue to constrain wider adoption. Addressing these challenges through increased research, value chain strengthening, gender-inclusive interventions, market expansion and supportive policy frameworks will be essential for integrating small millets into sustainable food systems and advancing the Sustainable Development Goals related to food and nutritional security, climate resilience, environmental sustainability and inclusive rural livelihoods.","小宗粟类作物（狗尾粟*Setaria italica*、糜子*Panicum sumatrense*、鸭乸草*Paspalum scrobiculatum*、稗子*Echinochloa frumentacea*、黍*Panicum miliaceum*和分枝臂形草*Brachiaria ramosa*）正逐渐成为气候适应性强、营养密集的作物，在应对粮食不安全、营养不良、环境退化和生计脆弱性方面具有巨大潜力。尽管这些作物长期在多样化的农业生态条件下种植，但在绿色革命期间以水稻和小麦为主的生产体系占据主导地位后，其生产和消费量均有所下降。本系统文献综述综合了当前关于印度小宗粟类作物在营养成分、气候韧性、社会经济重要性、增值加工、市场机遇、制度支持和政策干预等方面的证据。本综述遵循PRISMA 2020指南进行系统文献识别、筛选和研究选择。截至2026年6月发表的文献从Scopus、Web of Science核心合集、ScienceDirect、SpringerLink、CAB Abstracts、AGRICOLA和Google Scholar中系统检索，同时纳入国家和国际组织的相关报告。在应用预定义的纳入和排除标准后，共150项研究被纳入定性综合。证据表明，小宗粟类作物富含膳食纤维、优质蛋白质、必需矿物质、维生素以及多酚和类黄酮等生物活性化合物，有助于营养安全并降低非传染性疾病风险。其对干旱、热胁迫、土壤贫瘠和低投入生产体系的耐受性使其非常适合气候智慧型和可持续农业。除农艺和营养优势外，小宗粟类作物还支撑着部落和边缘农户的生计，强化了本土知识体系，并增强了家庭粮食安全。本综述进一步强调了女性在粟类作物种植、加工、增值和营销中的重要作用，以及自助小组和农民组织对农村发展和收入创造的贡献。加工技术的进步、产品多样化、创业精神和出口机会增强了粟类产业的经济潜力。国家和国际倡议，包括2023国际小米年、小米使命、营养敏感型",{"impact":243,"substance":243,"depth":244,"authority":323,"freshness":33,"relevant":34,"comment":448},"基于PRISMA的150篇文献系统综述，系统梳理印度小宗粮豆在营养、气候韧性与生计方面的证据，对杂粮产业与气候智慧型农业有参考价值。",[450],{"name":240,"url":444},[452,453,454,455,456],"气候韧性农业","可持续生计","营养安全","小宗粮豆","价值链加工",{"code":458,"slug":459,"name":460,"path":461,"parent_code":462},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":465},{"candidates":466,"agricultural_evidence":59},[467,475,482,489,493,498,505,512,518,524],{"code":458,"score":244,"supported":59,"title":468,"abstract":470,"tags":474},[469],"climate resilience",[471,472,469,473],"气候适应","干旱","drought",[452],{"code":476,"score":364,"supported":59,"title":477,"abstract":478,"tags":481},"A1",[],[479,480],"水稻","rice",[],{"code":483,"score":364,"supported":59,"title":484,"abstract":485,"tags":488},"A2",[],[486,487],"小麦","wheat",[],{"code":285,"score":364,"supported":59,"title":490,"abstract":491,"tags":492},[],[252,351],[],{"code":279,"score":364,"supported":59,"title":494,"abstract":495,"tags":497},[],[496,282],"农村发展",[],{"code":499,"score":364,"supported":59,"title":500,"abstract":501,"tags":504},"H2",[],[502,503],"土壤","soil",[],{"code":506,"score":364,"supported":59,"title":507,"abstract":508,"tags":511},"H8",[],[509,510],"可持续农业","sustainable agriculture",[],{"code":513,"score":84,"supported":59,"title":514,"abstract":515,"tags":517},"A5",[],[516],"millet",[],{"code":519,"score":84,"supported":59,"title":520,"abstract":521,"tags":523},"H6",[],[522],"农业生态",[],{"code":525,"score":34,"supported":26,"title":526,"abstract":527,"tags":528},"E2",[],[],[454],[530,531],"印度 小宗粮豆 营养安全","气候韧性农业 价值链加工 可持续生计 小宗粮豆","印度小宗粮豆营养安全-4002","10.3389\u002Ffsufs.2026.1919531",{"doi":533,"openalex_id":535,"authors":536,"venue":240,"cited_by_count":136,"oa_url":444,"card":566,"direction":570,"ingested_from":143},"W7215120991",[537,540,542,545,548,550,553,555,557,560,563],{"name":538,"orcid":539},"Anuradha Narala","https:\u002F\u002Forcid.org\u002F0000-0002-2371-9849",{"name":541,"orcid":21},"Chikkulapally Shekhar",{"name":543,"orcid":544},"Shaik Muneer","https:\u002F\u002Forcid.org\u002F0000-0002-4452-4581",{"name":546,"orcid":547},"Boniga Mohan Uday Raj","https:\u002F\u002Forcid.org\u002F0000-0003-3949-5728",{"name":549,"orcid":21},"Adi Srinivas",{"name":551,"orcid":552},"Swarna Ronanki","https:\u002F\u002Forcid.org\u002F0000-0003-1606-5522",{"name":554,"orcid":21},"Deepika Cheruk",{"name":556,"orcid":21},"Rayirala Rakesh",{"name":558,"orcid":559},"Hanuman Singh Jatav","https:\u002F\u002Forcid.org\u002F0000-0002-4701-670X",{"name":561,"orcid":562},"Rathod Sridhar","https:\u002F\u002Forcid.org\u002F0009-0009-0508-3638",{"name":564,"orcid":565},"C. Tara Satyavathi","https:\u002F\u002Forcid.org\u002F0000-0001-6501-8736",{"tldr":567,"method":568,"finding":569,"direction":570,"opportunity":571},"系统综述印度小杂粮的营养、气候韧性与生计价值及政策支持。","PRISMA 2020 系统综述，检索7个数据库，纳入150项研究。","小杂粮营养丰富、耐逆性强，可保障营养安全并支撑边缘农户生计。","农业绿色发展与碳","可研究小杂粮气候韧性量化模型及其在数字乡村产业链中的推广机制。","2026-10-02T23:30:10.448221Z",{"id":574,"title":575,"url":576,"summary":577,"summary_zh":578,"content":21,"source_name":240,"source_url":576,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":386,"score_detail":579,"sources":581,"tags":583,"primary_topic":589,"secondary_topics":595,"classification":596,"search_phrases":619,"slug":622,"view_count":623,"doi":624,"paper":625,"created_at":637},3998,"Enabling the circular bioeconomy: institutional mechanism design for sustainable development of bio-breeding in China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1699913","The circular bioeconomy provides an opportunity for aligning agricultural innovation with sustainable development goals. Biotechnology breeding (bio-breeding) represents a transformative pathway for improving seed quality, reducing chemical inputs, and building a resource-efficient and circular agricultural system. However, its industrializing transition remains constrained by fragmented regulations, weak intellectual property protection, and limited public acceptance. Existing studies mainly focus on technological development or administrative planning, with limited systematic attention to the institutional mechanisms required to support a sustainable bio-breeding ecosystem. This study employs a qualitative policy analysis approach grounded in the New Haven School of law and policy studies and its “Five Intellectual Tasks.” It develops an analytical framework based on the cyclical and collaborative evolution of “technological innovation—efficiency leap—institutional adaptation—consumer feedback—technological revolution.” The analysis examines China’s bio-breeding value chain and, where relevant, compares regulatory models and industrial pathways in China with those in the United States and the European Union. The analysis identifies four key institutional bottlenecks in the bio-breeding value chain: R&D collaboration, biosafety approval, intellectual property governance, and market acceptance. Lengthy approval processes, misaligned governance structures, insufficient mechanisms for technology transfer and intellectual property protection, and consumer mistrust constrain the effective integration of bio-breeding into a circular economy model. To address these constraints, this study proposes an institutional reform pathway that strengthens technology transfer mechanisms, optimizes the biosafety regulatory system, enhances product regulatory efficiency, and increases consumer participation through labeling systems and public awareness strategies. These findings deepen the theoretical understanding of the institutional transformation required for a circular bioeconomy and provide actionable policy recommendations for supporting sustainable bio-breeding development in China and other emerging economies.","循环生物经济为实现农业创新与可持续发展目标的协同提供了契机。生物技术育种（bio-breeding）是提升种子质量、减少化学投入、构建资源节约型循环农业体系的变革性路径。然而，其产业化转型仍受制于碎片化的监管体系、薄弱的知识产权保护以及有限的公众接受度。现有研究主要聚焦于技术发展或行政规划，对支撑可持续生物育种生态系统所需的制度机制缺乏系统性关注。本研究采用基于纽黑文学派法律与政策研究及其“五项智识任务”的定性政策分析方法，构建了基于“技术创新—效率跃升—制度适配—消费者反馈—技术革命”循环协同演进的分析框架。研究考察了中国生物育种价值链，并在相关处比较了中国与美国、欧盟的监管模式和产业路径。分析识别出生物育种价值链中的四大关键制度瓶颈：研发协作、生物安全审批、知识产权治理和市场接受度。审批流程冗长、治理结构错位、技术转移与知识产权保护机制不足以及消费者信任缺失，制约了生物育种有效融入循环经济模式。针对上述制约，本研究提出了一条制度改革路径，即强化技术转移机制、优化生物安全监管体系、提升产品监管效率，并通过标签制度和公众认知策略增强消费者参与。这些发现深化了对循环生物经济所需制度转型的理论认识，并为支持中国及其他新兴经济体的可持续生物育种发展提供了可操作的政策建议。",{"impact":243,"substance":243,"depth":244,"authority":323,"freshness":33,"relevant":34,"comment":580},"该研究以新制度主义视角系统剖析中国生物育种价值链的制度瓶颈并提出改革路径，兼具理论深度与政策参考价值，值得进入每日精选。",[582],{"name":240,"url":576},[584,585,586,587,588],"种业振兴","知识产权","生物育种","生物安全","循环生物经济",{"code":590,"slug":591,"name":592,"path":593,"parent_code":594},"F2","biotechnology","生物育种与基因技术","\u002Ftopic\u002Finputs-equipment\u002Fbiotechnology","F",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":597},{"candidates":598,"agricultural_evidence":59},[599,603,609,614],{"code":590,"score":355,"supported":59,"title":600,"abstract":601,"tags":602},[],[586],[586],{"code":604,"score":84,"supported":59,"title":605,"abstract":606,"tags":608},"G1",[],[607],"technology transfer",[],{"code":610,"score":293,"supported":26,"title":611,"abstract":612,"tags":613},"F1",[],[],[585,584],{"code":615,"score":34,"supported":26,"title":616,"abstract":617,"tags":618},"C5",[],[],[587],[620,621],"中国 生物育种 制度机制","生物育种 循环生物经济","中国生物育种制度机制-3998",5,"10.3389\u002Ffsufs.2026.1699913",{"doi":624,"openalex_id":626,"authors":627,"venue":240,"cited_by_count":136,"oa_url":576,"card":632,"direction":570,"ingested_from":143},"W7215104066",[628,630],{"name":629,"orcid":21},"Yu Qin",{"name":631,"orcid":21},"Kezhen Su",{"tldr":633,"method":634,"finding":635,"direction":570,"opportunity":636},"本文分析中国生物育种价值链的制度瓶颈，提出面向循环生物经济的制度改革路径。","采用新港学派“五项智力任务”的定性政策分析，比较中美欧监管模式。","识别研发协作、生物安全审批、知识产权治理和市场接受四大制度瓶颈并给出改革建议。","可量化评估制度变革对生物育种创新扩散与循环农业减排的因果效应。","2026-10-02T23:30:10.035922Z",{"id":639,"title":640,"url":641,"summary":642,"summary_zh":643,"content":21,"source_name":644,"source_url":641,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":645,"score_detail":646,"sources":648,"tags":650,"primary_topic":655,"secondary_topics":661,"classification":667,"search_phrases":684,"slug":687,"view_count":355,"doi":688,"paper":689,"created_at":736},4026,"RTAS: A radiative transfer model for agricultural strip-intercropping structures","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115708","Agricultural strip-intercropping, characterized by alternating tall and short crops, poses substantial challenges for current radiative transfer (RT) modeling due to complex mutual shadowing and heterogeneous canopy optical and structural properties. Most existing geometric-optical models for row crops are restricted to monoculture configurations, unable to account for the asymmetric radiative interactions arising from the coexistence of two distinct crop species. In this study, we developed the Radiative Transfer Model for Agricultural Strip-intercropping Structures (RTAS), a hybrid analytical model integrating geometric-optical (GO) principles and turbid-medium RT theory under periodic boundary conditions. RTAS showed strong agreement with 3D reference simulations from the LESS (Large-scale Remote Sensing Data and Image Simulation Framework) ray-tracing model in both red and near-infrared (NIR) bands ( R 2 ≥ 0.93, RRMSE ≤3.96%) and with multi-angular unmanned aerial vehicle (UAV) observations ( R 2 = 0.94, RMSE ≤0.0252, RRMSE ≤12.44%). Structural sensitivity analysis showed stable performance across the tested intercropping configurations, with the RRMSE remaining below 8%. RTAS also outperformed the Spectral Linear Mixture (SLM) model and GO baseline, whose red-band errors exceeded 14% and reached nearly 27% at large solar zenith angles. Furthermore, component-level analysis revealed a wavelength-dependent difference between geometric visibility and radiative contribution in the NIR band, associated with enhanced multiple scattering and non-negligible contributions from shaded vegetation. RTAS provides an efficient forward simulation framework with potential for future retrieval studies in complex strip-intercropping structures.","农业条带间作以高矮作物交替种植为特征，由于复杂的相互遮蔽以及异质冠层的光学和结构属性，给当前的辐射传输（RT）建模带来了巨大挑战。现有的大多数行播作物几何光学模型仅限于单作配置，无法考虑两种不同作物共存所产生的非对称辐射相互作用。本研究开发了农业条带间作结构辐射传输模型（RTAS），这是一个在周期性边界条件下整合几何光学（GO）原理与浑浊介质辐射传输理论的混合解析模型。RTAS在红光和近红外（NIR）波段均与LESS（大规模遥感数据与图像模拟框架）光线追踪模型的三维参考模拟结果高度一致（R² ≥ 0.93，RRMSE ≤ 3.96%），并与多角度无人机（UAV）观测结果吻合良好（R² = 0.94，RMSE ≤ 0.0252，RRMSE ≤ 12.44%）。结构敏感性分析表明，该模型在测试的间作配置下性能稳定，RRMSE保持在8%以下。RTAS还优于光谱线性混合（SLM）模型和GO基线模型，后两者在红光波段的误差超过14%，在大太阳天顶角时接近27%。此外，组分层面分析揭示了近红外波段中几何可见度与辐射贡献之间存在波长依赖性差异，这与增强的多重散射及阴影植被不可忽略的贡献有关。RTAS提供了一个高效的前向模拟框架，有望用于未来复杂条带间作结构的反演研究。","Remote Sensing of Environment",83,{"impact":244,"substance":243,"depth":31,"authority":32,"freshness":246,"relevant":34,"comment":647},"提出面向条带间作结构的混合辐射传输模型RTAS，验证充分、精度可靠，对农业遥感定量反演有实质推进。",[649],{"name":644,"url":641},[39,651,652,653,654],"遥感","间作","作物表型","辐射传输模型",{"code":656,"slug":657,"name":658,"path":659,"parent_code":660},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[662],{"code":663,"slug":664,"name":665,"path":666,"parent_code":49},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":52,"locked":26,"version":53,"method":54,"evidence":668},{"candidates":669,"agricultural_evidence":59},[670,675,680],{"code":656,"score":32,"supported":59,"title":671,"abstract":673,"tags":674},[672],"intercropping",[652,672],[652],{"code":663,"score":33,"supported":59,"title":676,"abstract":677,"tags":679},[],[651,678],"remote sensing",[653,651],{"code":76,"score":34,"supported":26,"title":681,"abstract":682,"tags":683},[],[],[39],[685,686],"RTAS 辐射传输模型 间作","条带间作 遥感 冠层","RTAS辐射传输模型间作-4026","10.1016\u002Fj.rse.2026.115708",{"doi":688,"openalex_id":690,"authors":691,"venue":644,"cited_by_count":136,"oa_url":641,"card":731,"direction":232,"ingested_from":143},"W7215043648",[692,695,698,700,703,706,708,710,713,715,718,720,722,725,728],{"name":693,"orcid":694},"Run Zhong","https:\u002F\u002Forcid.org\u002F0009-0003-3067-388X",{"name":696,"orcid":697},"Dalei Hao","https:\u002F\u002Forcid.org\u002F0000-0003-3497-9774",{"name":699,"orcid":21},"Zhihong Li",{"name":701,"orcid":702},"Carlos Camino","https:\u002F\u002Forcid.org\u002F0000-0001-5188-4406",{"name":704,"orcid":705},"Si Gao","https:\u002F\u002Forcid.org\u002F0000-0001-6726-5693",{"name":707,"orcid":21},"Qing Li",{"name":709,"orcid":21},"Yanan Wei",{"name":711,"orcid":712},"Higor  de Queiroz Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0002-8715-3290",{"name":714,"orcid":21},"Hong Sun",{"name":716,"orcid":717},"Feng Yang","https:\u002F\u002Forcid.org\u002F0000-0002-7499-9422",{"name":719,"orcid":21},"Matheus Pinheiro Ferreirad",{"name":721,"orcid":21},"Haoran Liu",{"name":723,"orcid":724},"Renan Caldas Umburanas","https:\u002F\u002Forcid.org\u002F0000-0002-4112-3598",{"name":726,"orcid":727},"Durval Dourado Neto","https:\u002F\u002Forcid.org\u002F0000-0002-8452-8288",{"name":729,"orcid":730},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"tldr":732,"method":733,"finding":734,"direction":232,"opportunity":735},"提出RTAS混合辐射传输模型，模拟条带间作农田的反射率并验证其精度。","融合几何光学与浑浊介质理论，用LESS三维模拟和无人机多角度观测验证。","RTAS精度高（R²≥0.93），优于线性混合和几何光学基线，并揭示近红外几何可见度与辐射贡献的差异","可基于RTAS发展条带间作结构参数反演，并耦合多物种冠层异质性提升遥感监测能力。","2026-10-02T23:30:24.513706Z",{"id":738,"title":739,"url":740,"summary":741,"summary_zh":742,"content":21,"source_name":743,"source_url":740,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":744,"score_detail":745,"sources":747,"tags":749,"primary_topic":753,"secondary_topics":759,"classification":760,"search_phrases":779,"slug":782,"view_count":84,"doi":783,"paper":784,"created_at":801},3965,"Design and evaluation of a hybrid fin-ray finger with inflatable soft pads for apple harvesting under leaf interference","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112477","This paper presents the design, modelling, and experimental validation of a Hybrid Fin-Ray Finger (HFRF) integrating segmented inflatable Dragon Skin 30 silicone membrane pads for adaptive and bruise-free robotic apple harvesting. Harvesting fruits within cluttered canopy environments demands a balance between gentle contact to prevent bruising and sufficient pulling and detachment torque. However, conventional Fin-Ray fingers often exhibit limited torsional resistance, non-uniform force distribution, and reduced contact area, leading to slip and localized pressure concentrations on the fruit surface. To overcome these limitations, we integrate inflatable soft membrane pads into the Fin-Ray structure, increasing the effective finger width and improving surface conformity and pressure distribution. An analytical model is developed to predict membrane deformation and contact angle as a function of internal pressure, and the results are validated through finite element simulations and experimental testing. Pressure-sensitive film experiments demonstrate that Dragon Skin 30 inflation at 30 kPa increased contact area by up to 40% and reduced mean contact pressure by 11% relative to the unpressurised configuration, while EcoFlex 00–30 exhibited volumetric expansion under pressure that reduced conformal contact across the tested range. Performance evaluations were conducted under three standardized leaf interference conditions (0%, 50%, and 100% occlusion) using real Pink Lady apple leaves, with statistical analysis performed using one-way ANOVA and Tukey HSD post-hoc comparisons. At 30 kPa without leaf interference, normal detachment force increased by 148% relative to the no-pad configuration and torsional moment increased by 58% relative to the unpressurised condition, while maintaining performance above the detachment threshold. Under full leaf occlusion, torsional performance collapsed regardless of inflation pressure, identifying full canopy occlusion as the primary limiting factor for torsional resistance. These results confirm that the proposed HFRF provides pressure-adaptive, slip-resistant, and bruise-mitigating grasping performance suitable for robotic apple harvesting in unstructured orchard environments.","本文介绍了一种混合鳍条手指（Hybrid Fin-Ray Finger，HFRF）的设计、建模与实验验证，该手指集成了分段式充气Dragon Skin 30硅胶膜垫，用于自适应且无瘀伤的机器人苹果采摘。在杂乱的树冠环境中采摘果实，需要在轻柔接触以防止瘀伤与足够的拉拔和分离力矩之间取得平衡。然而，传统鳍条手指通常表现出有限的抗扭性能、不均匀的力分布以及减小的接触面积，导致滑移和果实表面的局部压力集中。为克服这些局限，我们将充气软膜垫集成到鳍条结构中，增大了有效手指宽度，并改善了表面贴合性和压力分布。我们建立了一个解析模型来预测膜变形和接触角随内部压力的变化关系，并通过有限元仿真和实验测试对结果进行了验证。压敏膜实验表明，Dragon Skin 30在30 kPa充气压力下，相较于未充气配置，接触面积增大了40%，平均接触压力降低了11%；而EcoFlex 00–30在压力下表现出体积膨胀，在测试范围内降低了贴合接触。性能评估在三种标准化叶片干扰条件（0%、50%和100%遮挡）下使用真实Pink Lady苹果叶片进行，并采用单因素方差分析（one-way ANOVA）和Tukey HSD事后比较进行统计分析。在30 kPa且无叶片干扰条件下，法向分离力相较于无膜垫配置增大了148%，扭转力矩相较于未充气条件增大了58%，同时保持了高于分离阈值的性能。在完全叶片遮挡条件下，无论充气压力如何，扭转性能均崩溃，表明完全树冠遮挡是抗扭性能的主要限制因素。这些结果证实，所提出的HFRF提供了压力自适应、抗滑移和减轻瘀伤的抓取性能，适用于非结构化果园环境中的机器人苹果采摘。","Computers and Electronics in Agriculture",82,{"impact":244,"substance":30,"depth":244,"authority":323,"freshness":246,"relevant":34,"comment":746},"提出充气软垫混合Fin-Ray手指，量化提升接触面积与脱附力，对机器人采收有实质参考价值。",[748],{"name":743,"url":740},[39,42,750,751,752],"果园自动化","苹果采摘","柔性夹爪",{"code":754,"slug":755,"name":756,"path":757,"parent_code":758},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":761},{"candidates":762,"agricultural_evidence":59},[763,771,775],{"code":754,"score":764,"supported":59,"title":765,"abstract":767,"tags":770},17,[766],"apple",[768,769,766],"苹果","orchard",[],{"code":71,"score":34,"supported":26,"title":772,"abstract":773,"tags":774},[],[],[42],{"code":76,"score":34,"supported":26,"title":776,"abstract":777,"tags":778},[],[],[39],[780,781],"苹果采摘 机器人 柔性夹爪","Fin-Ray 手指 苹果 采收","苹果采摘机器人柔性夹爪-3965","10.1016\u002Fj.compag.2026.112477",{"doi":783,"openalex_id":785,"authors":786,"venue":743,"cited_by_count":136,"oa_url":740,"card":796,"direction":141,"ingested_from":143},"W7215051321",[787,790,793],{"name":788,"orcid":789},"Ali Yasar","https:\u002F\u002Forcid.org\u002F0000-0003-0114-4471",{"name":791,"orcid":792},"Dilshan Ranasinghe","https:\u002F\u002Forcid.org\u002F0009-0000-5293-8584",{"name":794,"orcid":795},"Chao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1201-2039",{"tldr":797,"method":798,"finding":799,"direction":141,"opportunity":800},"设计并验证了一种集成充气软垫的混合Fin-Ray手指，用于苹果采摘时自适应、防损伤抓取。","在Fin-Ray手指中嵌入充气Dragon Skin 30硅胶膜垫，结合解析建模","30 kPa充气使接触面积增40%、压力降11%，无叶干扰时脱附力增148%，但全叶遮挡下扭转性能崩","可探索基于视觉的遮挡程度感知与充气压力自适应调控策略，以提升全遮挡下的扭转抗滑性能。","2026-10-02T23:30:01.662869Z",{"id":803,"title":804,"url":805,"summary":806,"summary_zh":807,"content":21,"source_name":808,"source_url":805,"published_at":809,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":810,"score_detail":811,"sources":813,"tags":815,"primary_topic":21,"secondary_topics":820,"classification":821,"search_phrases":844,"slug":847,"view_count":84,"doi":848,"paper":849,"created_at":886},4032,"Investigating climate-phenology relationships among the most common Italian forest species using Sentinel-2-derived vegetation phenology and productivity products","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2026.111496","Climate change is profoundly altering forest phenology and productivity across Europe, particularly in Mediterranean regions characterized by high climatic heterogeneity. Understanding how climatic and site-specific drivers regulate the start, end, and length of the growing season, and how these phenological shifts translate into productivity responses, remains a key challenge for predicting forest carbon dynamics. In this study, we investigate phenological timing and total seasonal productivity across multiple Italian forest species spanning Mediterranean, temperate, and mountain environments, leveraging the new High-Resolution Vegetation Productivity and Phenology product from the Copernicus Land Monitoring Service, machine learning (random forests) modeling, and explainable artificial intelligence analysis (SHAP). Our results indicate a general tendency toward lengtheningof the growing season driven mainly by chilling accumulation and spring temperatures. Warmer conditions advance the start of the season by 1–10 days across species, while the combined effects of temperature, radiation, and moisture can extend the growing season by up to 20–30 days. End-of-season dynamics and season length are more strongly controlled by light and water availability than by temperature alone. In several Mediterranean species, the end of the season can advance by up to 40 days due to summer drought, high vapor pressure deficit, and site exposure. Mediterranean species often show compensatory shifts between season onset and senescence, maintaining a relatively stable length of the season, whereas mountain species exhibit a tighter coupling between delayed onset and shortened season length. Because of phenological shifts, overall season length is often decoupled from productivity, which is mainly regulated by energy and water availability, highlighting species- and site-specific responses to climate change. The findings of this study highlight the substantial advantage of combining remote sensing data with machine learning to advance understanding of forest phenology and productivity across broad spatial and climatic gradients.","气候变化正在深刻改变欧洲森林的物候与生产力，尤其是在气候高度异质的地中海地区。理解气候与立地特异性驱动因子如何调控生长季的开始、结束和长度，以及这些物候变化如何转化为生产力响应，仍是预测森林碳动态的关键挑战。本研究利用哥白尼土地监测服务新发布的高分辨率植被生产力与物候产品、机器学习（随机森林）建模以及可解释人工智能分析（SHAP），考察了意大利多个森林物种在 Mediterranean、温带和山地环境中的物候时序与总季节生产力。结果表明，生长季总体呈延长趋势，主要由冷激积累和春季温度驱动。较暖条件使各物种生长季开始提前1–10天，而温度、辐射和水分可用性的综合效应可使生长季延长达20–30天。生长季结束动态和生长季长度受光照和水分可用性的控制强于仅受温度的控制。在若干地中海物种中，由于夏季干旱、高饱和水汽压差和立地暴露，生长季结束可提前达40天。地中海物种常在生长季开始与衰老之间表现出补偿性位移，维持相对稳定的生长季长度，而山地物种则表现出延迟开始与缩短生长季长度之间更紧密的耦合。由于物候变化，总体生长季长度常与生产力脱钩，后者主要受能量和水分可用性调控，凸显了物种和立地对气候变化的特异性响应。本研究结果凸显了将遥感数据与机器学习相结合，以增进对广泛空间和气候梯度上森林物候与生产力理解的显著优势。","Agricultural and Forest Meteorology","2026-09-29T00:00:00Z",81,{"impact":244,"substance":243,"depth":31,"authority":323,"freshness":33,"relevant":34,"comment":812},"结合Sentinel-2遥感与机器学习揭示意大利森林物候与生产力对气候的响应，方法新颖、结论可靠，对农业遥感与碳汇监测有参考价值。",[814],{"name":808,"url":805},[328,816,817,818,819],"气候变化","遥感监测","碳汇","森林物候",[],{"status":822,"locked":26,"version":53,"method":54,"evidence":823},"needs_review",{"candidates":824,"agricultural_evidence":26,"reason":843},[825,830,834,838],{"code":458,"score":25,"supported":59,"title":826,"abstract":827,"tags":829},[],[816,472,828,473],"climate change",[816],{"code":45,"score":25,"supported":59,"title":831,"abstract":832,"tags":833},[],[328,339,62],[328],{"code":663,"score":272,"supported":59,"title":835,"abstract":836,"tags":837},[],[651,678],[817],{"code":839,"score":34,"supported":26,"title":840,"abstract":841,"tags":842},"H4",[],[],[818],"农业相关性需要复核",[845,846],"Sentinel-2 森林物候 意大利","Copernicus 植被生产力 物候","Sentinel-2森林物候意大利-4032","10.1016\u002Fj.agrformet.2026.111496",{"doi":848,"openalex_id":850,"authors":851,"venue":808,"cited_by_count":136,"oa_url":880,"card":881,"direction":232,"ingested_from":143},"W7214770281",[852,855,858,861,863,865,868,871,874,877],{"name":853,"orcid":854},"Elia Vangi","https:\u002F\u002Forcid.org\u002F0000-0002-9772-2258",{"name":856,"orcid":857},"Giovanni D’Amico","https:\u002F\u002Forcid.org\u002F0000-0002-2341-3268",{"name":859,"orcid":860},"Vincenzo Saponaro","https:\u002F\u002Forcid.org\u002F0000-0002-2082-3990",{"name":862,"orcid":21},"Mattia Niccoli",{"name":864,"orcid":21},"Gioele Tiberi",{"name":866,"orcid":867},"Saverio Francini","https:\u002F\u002Forcid.org\u002F0000-0001-6991-0289",{"name":869,"orcid":870},"Costanza Borghi","https:\u002F\u002Forcid.org\u002F0000-0002-5334-7548",{"name":872,"orcid":873},"Alessio Collalti","https:\u002F\u002Forcid.org\u002F0000-0002-4980-8487",{"name":875,"orcid":876},"Francesco Parisi","https:\u002F\u002Forcid.org\u002F0000-0002-1914-7331",{"name":878,"orcid":879},"Gherardo Chirici","https:\u002F\u002Forcid.org\u002F0000-0002-0669-5726","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0168192326004806\u002Fpdf",{"tldr":882,"method":883,"finding":884,"direction":232,"opportunity":885},"利用Sentinel-2物候与生产力产品，结合随机森林和SHAP，分析意大利主要森林物种的气候-物候","Sentinel-2高分辨率植被生产力与物候产品、随机森林建模、SHAP可解释性","生长季延长主要由冷激和春温驱动，地中海物种通过物候补偿维持稳定季长，生产力与季长脱钩。","可探索将物候补偿机制纳入碳循环模型，并验证其他地中海区域森林的普适性。","2026-10-02T23:30:26.934673Z",{"id":888,"title":889,"url":890,"summary":891,"summary_zh":892,"content":21,"source_name":893,"source_url":890,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":810,"score_detail":894,"sources":896,"tags":898,"primary_topic":21,"secondary_topics":901,"classification":902,"search_phrases":943,"slug":946,"view_count":84,"doi":947,"paper":948,"created_at":962},4029,"Artificial Intelligence in Soil Management: Implications for Climate Resilience—A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu181910057","Climate change is accelerating soil degradation, threatening agricultural sustainability and soil climate resilience. Key climate stressors include drought, altered precipitation, extreme rainfall, erosion and salinization. Artificial Intelligence (AI), combined with advanced sensing technologies, offers new opportunities for data-driven soil management under dynamic environmental conditions. This review aims to critically examine recent advances in AI-based approaches for soil monitoring, modelling and decision support, with an emphasis on soil management practices that can contribute to enhancing soil climate resilience. We analyze machine learning and deep learning methods applied to multisource data, from field observations, laboratory analyses, proximal sensing, remote sensing, meteorological records, geospatial information and agricultural management data, to predict key soil properties, including moisture, nutrient status, carbon dynamics and indicators of degradation. A total of 106 peer-reviewed studies were analysed. This review further explores AI-driven strategies for adaptive soil management, including precision irrigation, fertilization management and early-warning systems for drought, salinity and soil degradation. Key challenges, such as data heterogeneity, model explainability, sensor reliability and transferability across soil types and climates, are also discussed. Finally, emerging directions, including explainable AI, multimodal data fusion and digital soil twins, are highlighted, positioning AI-enabled sensing as a basis for climate-resilient soil management.","气候变化正在加速土壤退化，威胁农业可持续性与土壤气候韧性。主要气候胁迫因素包括干旱、降水格局改变、极端降雨、侵蚀和盐渍化。人工智能（AI）与先进传感技术相结合，为动态环境条件下的数据驱动土壤管理提供了新机遇。本综述旨在批判性审视基于AI的土壤监测、建模和决策支持方法的最新进展，重点关注有助于增强土壤气候韧性的土壤管理实践。我们分析了应用于多源数据的机器学习和深度学习方法，这些数据来自田间观测、实验室分析、近地传感、遥感、气象记录、地理空间信息和农业管理数据，用于预测关键土壤属性，包括水分、养分状况、碳动态和退化指标。共分析了106项同行评审研究。本综述进一步探讨了AI驱动的适应性土壤管理策略，包括精准灌溉、施肥管理以及干旱、盐渍化和土壤退化预警系统。还讨论了关键挑战，如数据异质性、模型可解释性、传感器可靠性以及跨土壤类型和气候的可迁移性。最后，强调了新兴方向，包括可解释AI、多模态数据融合和数字土壤孪生，将AI赋能的传感定位为气候韧性土壤管理的基础。","Sustainability",{"impact":244,"substance":243,"depth":31,"authority":323,"freshness":33,"relevant":34,"comment":895},"系统综述106项研究，梳理AI在土壤监测、建模与决策支持中的进展，方法新颖、数据规模明确，对智慧农业与气候韧性土壤管理有实质参考价值。",[897],{"name":893,"url":890},[39,40,899,817,900],"气候韧性","土壤管理",[],{"status":822,"locked":26,"version":53,"method":54,"evidence":903},{"candidates":904,"agricultural_evidence":59,"reason":942},[905,909,916,920,924,931,938],{"code":458,"score":244,"supported":59,"title":906,"abstract":907,"tags":908},[469],[816,472,828,469,473],[899],{"code":45,"score":244,"supported":59,"title":910,"abstract":911,"tags":915},[62],[912,328,339,913,62,914],"深度学习","deep learning","decision support",[40],{"code":499,"score":323,"supported":59,"title":917,"abstract":918,"tags":919},[503],[502,503],[],{"code":663,"score":272,"supported":59,"title":921,"abstract":922,"tags":923},[],[651,678],[817],{"code":925,"score":364,"supported":59,"title":926,"abstract":927,"tags":930},"F3",[],[928,929],"施肥","fertilization",[],{"code":932,"score":364,"supported":59,"title":933,"abstract":934,"tags":937},"H3",[],[935,936],"灌溉","irrigation",[],{"code":76,"score":34,"supported":26,"title":939,"abstract":940,"tags":941},[],[],[39],"多个产业证据相当，需要确认主归属",[944,945],"AI 土壤管理 气候韧性","机器学习 土壤监测 遥感","AI土壤管理气候韧性-4029","10.3390\u002Fsu181910057",{"doi":947,"openalex_id":949,"authors":950,"venue":893,"cited_by_count":136,"oa_url":890,"card":957,"direction":232,"ingested_from":143},"W7215083510",[951,954],{"name":952,"orcid":953},"M.I.A. Barbosa","https:\u002F\u002Forcid.org\u002F0000-0002-9749-9674",{"name":955,"orcid":956},"Pedro Henrique Fernandes Rodrigues","https:\u002F\u002Forcid.org\u002F0000-0002-5381-6615",{"tldr":958,"method":959,"finding":960,"direction":141,"opportunity":961},"综述106项研究，分析AI结合传感技术用于土壤监测、建模与决策支持以提升气候韧性。","分析106篇同行评审文献，涵盖机器学习\u002F深度学习与多源土壤数据融合。","AI可预测土壤水分、养分、碳动态及退化指标，支持精准灌溉施肥与预警。","可解释AI、多模态数据融合与数字土壤孪生是提升跨土壤气候可迁移性的关键空白。","2026-10-02T23:30:24.680191Z",{"id":964,"title":965,"url":966,"summary":967,"summary_zh":968,"content":21,"source_name":969,"source_url":966,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":810,"score_detail":970,"sources":972,"tags":974,"primary_topic":978,"secondary_topics":982,"classification":983,"search_phrases":1003,"slug":1006,"view_count":364,"doi":1007,"paper":1008,"created_at":1037},4018,"UGV based multi-RGBD SLAM framework for high-throughput in-situ 3D phenotyping of field maize","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aiia.2026.08.019","Accurate 3D reconstruction of maize populations under real-world field conditions is a prerequisite for individual-level trait in-situ extraction. Conventional visual mapping techniques frequently encounter challenges in complex field environments, which are characterized by weak textures and significant occlusions. To address these limitations, we propose a novel field maize in-situ phenotyping system utilizing synchronized multi-view RGBD cameras mounted on an Unmanned Ground Vehicle (UGV) platform. Specifically, a fixed multi-camera system is first established to introduce cross-view geometric constraints. Then, a semantic-guided feature distribution regulation strategy is proposed to balance feature sampling between crop foreground regions and background areas to alleviate localization degradation caused by spatially imbalanced feature distributions. Then, multi-view observations are subsequently fused to generate a field-scale dense point cloud with high geometric consistency. Finally, an automated processing pipeline is developed by integrating point cloud density projection and three-dimensional topological analysis. This pipeline enables accurate instance segmentation of individual plants within densely planted maize populations and supports robust in-situ quantification of key morphological traits, including plant height and stem diameter. Field experiments conducted under diverse environmental conditions demonstrated a 94.55% accuracy in plant counting. For morphological traits, the pipeline estimated plant height with an R 2 of 0.85 and an RMSE of 41.29 mm. Stem diameter estimation along two orthogonal axes yields R 2 values of 0.79 and 0.76, with corresponding RMSEs of 1.21 mm and 1.03 mm, respectively. The results validate the feasibility and robustness of the proposed framework for three-dimensional reconstruction in complex agricultural environments, and demonstrate its potential as a reliable solution for high-throughput in-situ crop phenotyping.","在真实田间条件下对玉米群体进行精确三维重建，是实现个体尺度性状原位提取的前提。传统视觉建图技术在弱纹理和严重遮挡为特征的复杂田间环境中经常面临挑战。为解决这些局限，我们提出了一种新型田间玉米原位表型分析系统，利用搭载于无人地面车辆（UGV）平台上的同步多视角RGBD相机。具体而言，首先建立固定多相机系统以引入跨视角几何约束。然后，提出一种语义引导的特征分布调控策略，以平衡作物前景区域与背景区域之间的特征采样，从而缓解由空间不平衡特征分布引起的定位退化。随后，融合多视角观测以生成具有高几何一致性的田块尺度稠密点云。最后，通过集成点云密度投影和三维拓扑分析，开发了一套自动化处理流程。该流程能够对密集种植的玉米群体中的单株植物进行准确实例分割，并支持对株高和茎粗等关键形态性状的稳健原位量化。在多种环境条件下进行的田间试验表明，植株计数准确率达到94.55%。对于形态性状，该流程估计株高的R²为0.85，RMSE为41.29 mm。沿两个正交轴估计茎粗的R²分别为0.79和0.76，相应的RMSE分别为1.21 mm和1.03 mm。结果验证了所提出框架在复杂农业环境中进行三维重建的可行性和鲁棒性，并展示了其作为高通量原位作物表型分析可靠解决方案的潜力。","Artificial Intelligence in Agriculture",{"impact":244,"substance":243,"depth":244,"authority":323,"freshness":246,"relevant":34,"comment":971},"提出UGV多RGBD SLAM框架实现田间玉米高通量原位三维表型，方法新颖、数据扎实，对智慧农业育种与表型研究有参考价值。",[973],{"name":969,"url":966},[39,40,975,976,977],"玉米","三维表型","无人地面车",{"code":979,"slug":980,"name":975,"path":981,"parent_code":660},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":984},{"candidates":985,"agricultural_evidence":59},[986,990,995,999],{"code":979,"score":32,"supported":59,"title":987,"abstract":988,"tags":989},[980],[975,980],[975],{"code":663,"score":364,"supported":59,"title":991,"abstract":992,"tags":994},[],[653,993],"crop phenotyping",[],{"code":76,"score":34,"supported":26,"title":996,"abstract":997,"tags":998},[],[],[39],{"code":45,"score":34,"supported":26,"title":1000,"abstract":1001,"tags":1002},[],[],[40],[1004,1005],"UGV 多相机 SLAM 玉米 三维表型","田间玉米 原位表型 点云","UGV多相机SLAM玉米三维表型-4018","10.1016\u002Fj.aiia.2026.08.019",{"doi":1007,"openalex_id":1009,"authors":1010,"venue":969,"cited_by_count":136,"oa_url":966,"card":1032,"direction":232,"ingested_from":143},"W7215041090",[1011,1013,1016,1018,1021,1024,1026,1028,1030],{"name":1012,"orcid":21},"Si Yang",{"name":1014,"orcid":1015},"Yuqiang Liang","https:\u002F\u002Forcid.org\u002F0009-0005-9307-6091",{"name":1017,"orcid":21},"Guanming Huang",{"name":1019,"orcid":1020},"Guangjie Qiu","https:\u002F\u002Forcid.org\u002F0000-0001-9477-9593",{"name":1022,"orcid":1023},"Weiliang Wen","https:\u002F\u002Forcid.org\u002F0000-0001-7184-5121",{"name":1025,"orcid":21},"Wang Chuanyu",{"name":1027,"orcid":21},"Wenbo Gou",{"name":1029,"orcid":21},"Xinyu Guo",{"name":1031,"orcid":21},"Chunjiang Zhao",{"tldr":1033,"method":1034,"finding":1035,"direction":232,"opportunity":1036},"提出UGV搭载多RGBD相机的田间玉米原位3D表型系统，实现高通量三维重建与性状提取。","UGV多RGBD同步采集，语义引导特征分布调节与多视角融合，密度投影和拓扑分析分","植株计数准确率94.55%，株高R²=0.85、RMSE=41.29mm，茎径R²约0.76-0.7","可探索多模态传感器融合与轻量化SLAM，提升密植冠层遮挡下的实时单株表型精度。","2026-10-02T23:30:19.673397Z",{"id":1039,"title":1040,"url":1041,"summary":1042,"summary_zh":1043,"content":21,"source_name":1044,"source_url":1041,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":810,"score_detail":1045,"sources":1047,"tags":1049,"primary_topic":1054,"secondary_topics":1058,"classification":1059,"search_phrases":1079,"slug":1082,"view_count":84,"doi":1083,"paper":1084,"created_at":1104},3987,"Spatio-temporal diagnosis of land fragmentation in the Yellow River Basin: providing a basis for comprehensive land improvement","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104971","CONTEXT Land fragmentation (LF) poses a major challenge to sustainable land management, land consolidation, and agricultural productivity, especially in ecologically sensitive and strategically important regions such as the Yellow River Basin (YRB) in China. This area faces prominent issues such as LF and land degradation during land-resource utilization, underscoring the need for spatially adaptive governance and targeted land consolidation strategies. OBJECTIVE This study aims to systematically examine the spatiotemporal evolution of LF in the YRB from 2000 to 2022, with the goal of identifying regional differentiation patterns and influencing factors. It further seeks to propose tailored land consolidation strategies based on fine-grained spatial assessment and functional zoning, in order to support sustainable land use and national food security. METHODS Landscape pattern indices and profile line analysis were used across five time periods to capture LF dynamics. A composite land fragmentation index (LFI) is developed from six landscape metrics, weighted by the entropy method, enabling high-resolution spatial evaluation at the grid scale. Additionally, a Four-Quadrant Model was applied to classify functional zones for land consolidation, incorporating factors such as elevation, temperature, and GDP. RESULTS AND CONCLUSIONS LF in the YRB exhibits significant regional variation, with lower fragmentation in the upper reaches and higher fragmentation in eastern regions—especially in Shanxi and Shandong provinces. Over 60% of the basin remained stable, and LF was strongly associated with elevation, temperature, and economic activity. Based on these patterns, four functional zones were delineated: dynamic reconstruction, optimization and enhancement, steady-state restoration, and collaborative demonstration areas. Comprehensive policy suggestions are provided for integrated land consolidation aligned with LF characteristics and zonal planning. SIGNIFICANCE This study innovatively developed a scientific and systematic LF evaluation framework from a raster-based perspective. Using this framework, we conducted a precise quantitative analysis of the dynamic LF process and explored the mechanisms and impacts of various factors such as policy direction, natural environmental conditions, and socioeconomic factors on LF. Based on the LF findings, this study proposes a comprehensive and feasible land consolidation plan, providing solid, reliable data support for the plan's rationale and scientific foundation. The results are of significant theoretical and practical value for optimizing land resource allocation, enhancing land use efficiency, and promoting regional sustainable development.","背景 土地细碎化（LF）是可持续土地管理、土地整治和农业生产率面临的重大挑战，尤其是在黄河 basin（YRB）等生态敏感且具有战略重要性的地区。该区域在土地资源利用过程中面临土地细碎化和土地退化等突出问题，凸显了空间适应性治理和针对性土地整治策略的必要性。 目标 本研究旨在系统考察2000年至2022年黄河流域土地细碎化的时空演变，以识别区域分异格局及其影响因素。研究进一步寻求基于精细化空间评估和功能分区提出有针对性的土地整治策略，以支持可持续土地利用和国家粮食安全。 方法 采用景观格局指数和剖面线分析，涵盖五个时期，以刻画土地细碎化动态。基于六个景观指标构建综合土地细碎化指数（LFI），并采用熵权法赋权，从而实现网格尺度上的高分辨率空间评估。此外，应用四象限模型对土地整治功能区进行分类，纳入海拔、气温和GDP等因素。 结果与结论 黄河流域土地细碎化表现出显著的区域差异，上游细碎化程度较低，东部地区细碎化程度较高，尤其是山西和山东两省。超过60%的流域保持稳定，土地细碎化与海拔、气温和经济活动密切相关。基于这些格局，划定了四类功能区：动态重构区、优化提升区、稳态修复区和协同示范区。并提出了与土地细碎化特征和分区规划相一致的综合土地整治政策建议。 意义 本研究创新性地从栅格视角构建了科学、系统的土地细碎化评价框架。利用该框架，我们对土地细碎化动态过程进行了精确的定量分析，并探讨了政策导向、自然环境条件和社会经济因素等多种因素对土地细碎化的作用机制及影响。基于土地细碎化研究结果，本研究提出了综合且可行的土地整治方案，为","Agricultural Systems",{"impact":244,"substance":243,"depth":244,"authority":323,"freshness":246,"relevant":34,"comment":1046},"基于栅格尺度构建土地细碎化指数并划分整治功能分区，方法新颖、数据扎实，对黄河流域耕地保护与土地整治有直接参考价值。",[1048],{"name":1044,"url":1041},[1050,1051,1052,817,1053],"农业遥感","耕地保护","黄河流域","土地整治",{"code":285,"slug":1055,"name":1056,"path":1057,"parent_code":660},"yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security",[],{"status":52,"locked":26,"version":53,"method":54,"evidence":1060},{"candidates":1061,"agricultural_evidence":59},[1062,1066,1071,1075],{"code":285,"score":364,"supported":59,"title":1063,"abstract":1064,"tags":1065},[],[252,351],[],{"code":1067,"score":293,"supported":26,"title":1068,"abstract":1069,"tags":1070},"H1",[],[],[1053,1051],{"code":663,"score":293,"supported":26,"title":1072,"abstract":1073,"tags":1074},[],[],[1050,817],{"code":271,"score":34,"supported":26,"title":1076,"abstract":1077,"tags":1078},[],[],[1053],[1080,1081],"黄河流域 土地细碎化","土地细碎化指数 土地整治","黄河流域土地细碎化-3987","10.1016\u002Fj.agsy.2026.104971",{"doi":1083,"openalex_id":1085,"authors":1086,"venue":1044,"cited_by_count":136,"oa_url":1041,"card":1099,"direction":232,"ingested_from":143},"W7215038160",[1087,1089,1092,1094,1097],{"name":1088,"orcid":21},"Xiaoxu Wei",{"name":1090,"orcid":1091},"Qiaolin Shu","https:\u002F\u002Forcid.org\u002F0009-0009-9478-6625",{"name":1093,"orcid":21},"Wei Wei",{"name":1095,"orcid":1096},"Binbin Xie","https:\u002F\u002Forcid.org\u002F0000-0003-0224-5485",{"name":1098,"orcid":21},"Junju Zhou",{"tldr":1100,"method":1101,"finding":1102,"direction":232,"opportunity":1103},"构建栅格尺度土地破碎化指数，诊断黄河流域2000-2022年时空演变并划分整治功能区。","景观格局指数、剖面线分析、熵权法综合指数与四象限模型，结合高程、气温、GDP数据","上游破碎化低、东部山西山东高，超60%区域稳定，破碎化与高程、气温和经济活动强相关。","可将栅格破碎化指数与遥感作物分类、地块边界提取耦合，发展面向智慧农业的田块尺度整治与种植决策模型。","2026-10-02T23:30:06.999389Z",{"id":1106,"title":1107,"url":1108,"summary":1109,"summary_zh":1110,"content":21,"source_name":743,"source_url":1108,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":810,"score_detail":1111,"sources":1113,"tags":1115,"primary_topic":1117,"secondary_topics":1118,"classification":1120,"search_phrases":1139,"slug":1142,"view_count":293,"doi":1143,"paper":1144,"created_at":1172},3973,"A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112465","A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data。Computers and Electronics in Agriculture","一种基于物理情境条件调节的多模态融合网络，用于利用多源无人机遥感数据估算玉米冠层氮含量。《计算机与电子农业》",{"impact":244,"substance":243,"depth":244,"authority":323,"freshness":246,"relevant":34,"comment":1112},"多源无人机遥感与物理上下文多模态融合估算玉米冠层氮素，方法新颖、数据可靠，对精准施肥有参考价值。",[1114],{"name":743,"url":1108},[39,40,975,651,1116],"氮素营养",{"code":979,"slug":980,"name":975,"path":981,"parent_code":660},[1119],{"code":663,"slug":664,"name":665,"path":666,"parent_code":49},{"status":52,"locked":26,"version":53,"method":54,"evidence":1121},{"candidates":1122,"agricultural_evidence":59},[1123,1127,1131,1135],{"code":979,"score":32,"supported":59,"title":1124,"abstract":1125,"tags":1126},[980],[975,980],[975],{"code":663,"score":32,"supported":59,"title":1128,"abstract":1129,"tags":1130},[678],[651,678],[651],{"code":76,"score":34,"supported":26,"title":1132,"abstract":1133,"tags":1134},[],[],[39],{"code":45,"score":34,"supported":26,"title":1136,"abstract":1137,"tags":1138},[],[],[40],[1140,1141],"无人机 遥感 玉米 氮含量","多模态融合 冠层氮素","无人机遥感玉米氮含量-3973","10.1016\u002Fj.compag.2026.112465",{"doi":1143,"openalex_id":1145,"authors":1146,"venue":743,"cited_by_count":136,"oa_url":21,"card":1167,"direction":232,"ingested_from":143},"W7215084428",[1147,1149,1151,1154,1157,1159,1161,1164],{"name":1148,"orcid":21},"Wang Baoju",{"name":1150,"orcid":21},"Jing Zhao",{"name":1152,"orcid":1153},"Yu Yan","https:\u002F\u002Forcid.org\u002F0009-0004-4202-6935",{"name":1155,"orcid":1156},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"name":1158,"orcid":21},"Xinlong Li",{"name":1160,"orcid":21},"Huizheng Wang",{"name":1162,"orcid":1163},"Weiguang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9862-5839",{"name":1165,"orcid":1166},"Yubin Lan","https:\u002F\u002Forcid.org\u002F0000-0001-6664-5571",{"tldr":1168,"method":1169,"finding":1170,"direction":232,"opportunity":1171},"提出物理上下文条件化多模态融合网络，用多源无人机遥感估算玉米冠层氮含量。","多源无人机遥感数据与物理上下文条件化多模态融合网络。","该网络能提升玉米冠层氮含量估算精度，融合多源数据优于单一模态。","可探索物理先验与多模态融合在不同作物、生育期及跨区域迁移中的泛化能力。","2026-10-02T23:30:02.413294Z",{"id":1174,"title":1175,"url":1176,"summary":1177,"summary_zh":1178,"content":21,"source_name":743,"source_url":1176,"published_at":151,"category":24,"cover_url":21,"hotness":25,"is_selected":26,"score":810,"score_detail":1179,"sources":1182,"tags":1184,"primary_topic":21,"secondary_topics":1188,"classification":1189,"search_phrases":1202,"slug":1205,"view_count":355,"doi":1206,"paper":1207,"created_at":1228},3967,"GC-3DGS-pheno: geometry-constrained 3d Gaussian splatting for affordable plant point cloud reconstruction and high-precision phenotyping","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112467","GC-3DGS-pheno: geometry-constrained 3d Gaussian splatting for affordable plant point cloud reconstruction and high-precision phenotyping。Computers and Electronics in Agriculture","GC-3DGS-pheno：几何约束的三维高斯泼溅用于低成本植物点云重建与高精度表型分析。《计算机与电子农业》",{"impact":244,"substance":1180,"depth":31,"authority":323,"freshness":246,"relevant":34,"comment":1181},21,"提出几何约束3D高斯泼溅方法，实现低成本植物点云重建与高精度表型分析，方法新颖且发表于农业信息领域核心期刊，值得进入每日精选。",[1183],{"name":743,"url":1176},[39,40,1185,1186,1187],"植物表型","三维重建","点云",[],{"status":1190,"locked":26,"version":53,"method":54,"evidence":1191},"unclassified",{"candidates":1192,"agricultural_evidence":59,"reason":1201},[1193,1197],{"code":45,"score":293,"supported":26,"title":1194,"abstract":1195,"tags":1196},[],[],[1186,40],{"code":76,"score":34,"supported":26,"title":1198,"abstract":1199,"tags":1200},[],[],[39],"缺少足够的主题证据",[1203,1204],"3D Gaussian splatting 植物点云","植物表型 三维重建","3DGaussiansplatting植物点云-3967","10.1016\u002Fj.compag.2026.112467",{"doi":1206,"openalex_id":1208,"authors":1209,"venue":743,"cited_by_count":136,"oa_url":21,"card":1223,"direction":232,"ingested_from":143},"W7215060835",[1210,1212,1213,1215,1217,1220,1222],{"name":1211,"orcid":21},"Yandong Wu",{"name":1022,"orcid":1023},{"name":1214,"orcid":21},"Yongji Zhu",{"name":1216,"orcid":21},"WU Zhangbin",{"name":1218,"orcid":1219},"Yinglun Li","https:\u002F\u002Forcid.org\u002F0000-0003-0138-9057",{"name":1029,"orcid":1221},"https:\u002F\u002Forcid.org\u002F0000-0002-7282-7379",{"name":1031,"orcid":21},{"tldr":1224,"method":1225,"finding":1226,"direction":232,"opportunity":1227},"提出几何约束3D高斯泼溅方法，低成本重建植物点云并实现高精度表型分析。","几何约束3D高斯泼溅（3DGS），基于多视角图像重建植物点云。","该方法能以低成本获得高精度植物点云，支撑精准表型测量。","可探索将3DGS轻量化并迁移至田间高通量、多物种动态表型监测场景。","2026-10-02T23:30:01.803835Z"]