[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-246":3,"related-246":65},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":58,"slug":61,"view_count":62,"doi":63,"paper":8,"created_at":64},246,"改进BI-RRT*的黄鳝投喂机械臂路径规划算法——张春燕教授团队《智慧农业(中英文)》2026年第2期","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020","上海工程技术大学机械与汽车工程学院张春燕教授团队联合上海市农业科学院生态环境保护研究所,在《智慧农业(中英文)》2026年第2期发表论文,提出一种基于改进BI-RRT*(Bidirectional Rapidly-Exploring Random Tree Star)的受限空间路径规划算法。针对工厂化立体式黄鳝养殖饵料投喂机械臂在受限空间内路径规划速度慢、轨迹冗余度大、避障成功率低等问题,在BI-RRT*双向扩展策略基础上引入目标偏置策略减少随机采样点,同时结合改进的人工势场法融入目标点和随机点引力概念,通过自适应调节引力系数引导路径向目标节点扩展,有效避免算法陷入局部最优。在Matlab平台上对RRT、APF-RRT、BI-RRT及改进BI-RRT算法进行仿真分析,验证其在二维和三维空间下简单、受限及复杂环境中的优异表现。",null,"**摘要：**\n\n**【目的\u002F意义】** 针对工厂化立体式黄鳝养殖饵料投喂机械臂在受限空间内路径规划速度慢、轨迹冗余度大、避障成功率低等问题，提出一种基于改进BI-RRT*（Bidirectional Rapidly-Exploring Random Tree Star）的受限空间路径规划算法。 **【方法】** 在BI-RRT*算法双向扩展策略基础上，引入目标偏置策略，减少随机采样点。同时结合改进的人工势场法，融入目标点和随机点引力概念，通过自适应调节引力系数引导路径向目标节点扩展，有效避免算法陷入局部最优；最后通过渐进优化策略得到最佳轨迹。 **【结果和讨论】** 在Matlab平台上，对RRT*算法、APF-RRT*（ Artificial Potential Fields Rapidly-Exploring Random Tree Star）算法、BI-RRT*算法及改进BI-RRT*算法进行了仿真分析，研究其在二维和三维空间下简单、受限，以及复杂环境中的表现。仿真数据显示，在不同环境运行中，改进BI-RRT*算法均展现出显著的性能优势。基于黄鳝养殖投喂场景，搭建机械臂机器人操作系统仿真环境并进行受限空间下投喂试验。结果表明，与BI-RRT*算法相比，改进BI-RRT*算法平均运行时间减少41.6%，平均路径长度降低2.3％，平均节点数减少37.9％，以及投喂成功率提高6％。 **【结论】** 试验验证改进BI-RRT*算法在投喂任务中展现出更优性能，为工厂化立体式黄鳝养殖高效投饵工作提供了参考。\n\n**关键词:**[投喂机械臂,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[路径规划,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[改进BI-RRT*算法,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[目标偏置策略,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[改进人工势场法](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)\n\n**Abstract:**\n\n**[Objective]** In the eel (_Monopterus albus_) farming system used in feed distribution research of mechanical arm, the challenges included slow path planning speeds, excessive trajectory redundancy, and suboptimal obstacle avoidance success rates within confined operational spaces. To mitigate these issues, an improved path planning algorithm, based on the bidirectional rapidly-exploring random tree star (BI-RRT*) algorithm was proposed. The primary aim was to significantly enhance the motion efficiency and task success rate of robotic arms operating in complex, constrained environments. **[Methods]** The proposed improved BI-RRT* algorithm integrated an adaptive goal-biased strategy with an enhanced artificial potential field (APF) method. The algorithm's framework comprises three core components: a high-quality sampling strategy, an efficient search strategy, and a path optimization algorithm. For the high-quality sampling strategy, an adaptive goal-biased approach was introduced to overcome the limitations of inefficient random sampling and slow convergence rates characteristic of traditional BI-RRT algorithms in complex environments. This strategy dynamically adjusted the generation of sampling points, moving beyond purely random selection. Instead, it prioritized sampling regions in the vicinity of the target, guided by the target direction and a predefined bias probability. This mechanism substantially augmented the growth propensity of the search tree towards the target area, effectively reducing the stochasticity of random sampling and consequently accelerating the path search process. To enhance search efficiency and prevent the algorithm from converging to local optima, an improved APF was incorporated into the node expansion process. The APF was refined to achieve superior integration with the BI-RRT framework. During each new node expansion, in addition to considering the inherent random exploration characteristics of BI-RRT, a directional attractive field was superimposed. This attractive field not only originated from the ultimate target point but also factored in the current growth orientation of the search tree and localized environmental information. Specifically, a composite attractive function was devised, which synergized the attractive force exerted by the target point on the current node with the attraction from potential \"guide points\". Concurrently, the computation of the repulsive field was optimized to more precisely delineate the geometry and proximity of obstacles, thereby circumventing common issues such as \"oscillation\" and \"deadlock\" prevalent in traditional APF. Through this methodology, the algorithm was able to more effectively steer the search tree to circumvent obstacles and rapidly converge towards the target region, significantly bolstering the directness of the search and successfully preventing the algorithm from becoming ensnared in suboptimal local solutions. For the path optimization algorithm, following the generation of an initial feasible path, a greedy optimization strategy was employed for path pruning and smoothing. This was executed to yield an optimal path characterized by reduced length, enhanced smoothness, and improved conformity with the kinematic properties of the robotic arm. Path pruning was initially applied to eliminate redundant nodes; if a collision-free direct connection existed between two non-adjacent nodes, intermediate nodes were excised, thereby substantially abbreviating the path length. Subsequently, path smoothing techniques, such as B-spline curves or cubic spline interpolation, were introduced to enable the robotic arm to execute movements with greater stability and efficiency during actual operation, mitigating impact and vibration. This two-stage optimization procedure ensured that the final generated path was not merely feasible but also optimal across metrics of length, smoothness, and motion efficiency. **[Results and Discussions]** To comprehensively validate the performance of the proposed algorithm, a two-stage experimental verification was conducted. Initially, comparative simulations were performed in both two-dimensional (2D) and three-dimensional (3D) environments utilizing the Matlab platform. These simulation scenarios were meticulously engineered to encompass three archetypal environments—simple, complex, and narrow passages—thereby emulating the diverse obstacle configurations potentially encountered in industrialized eel aquaculture. The results demonstrated that, concerning both path planning speed and quality, the improved BI-RRT* algorithm significantly surpassed RRT, APF-RRT*, and traditional BI-RRT* algorithms across all tested environments, substantiatingthe theoretical superiority and inherent robustness of the improved BI-RRT* algorithm proposed in this study across varying complex environments. To further ascertain the engineering applicability and practical potential of the algorithm, an eel feeding robotic arm simulation system was meticulously constructed based on the robot operating system and MoveIt frameworks. This system precisely emulated the kinematics, dynamics, and obstacle distribution pertinent to an industrialized eel aquaculture environment. During simulated continuous feeding tasks, the improved BI-RRT* algorithm consistently exhibited impressive and outstanding performance. Its average running time was merely 2.1 s, representing a substantial 41.6% reduction compared to the traditional BI-RRT*. The average length of the planned path was recorded at only 1 680 mm, with an average of 180 nodes, indicating a significant reduction in path redundancy. Furthermore, the algorithm achieved an impressive obstacle avoidance success rate of 96% in complex confined spaces. These empirical findings not only validated the algorithm's effectiveness but also underscored its immense potential for practical engineering applications. **[Conclusions]** The experimental results conclusively demonstrated that the improved BI-RRT* algorithm significantly enhanced the path planning efficiency and trajectory quality of robotic arms operating within confined spaces. It also exhibited exceptionally high reliability in obstacle avoidance, thereby effectively addressing the automated feeding requirements of industrialized eel aquaculture. The algorithmic framework possessed considerable generality, offering valuable theoretical insights and technical precedents for resolving analogous robotic arm path planning challenges in other agricultural automation contexts.\n\n**Key words:**[feeding robotic arm,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[path planning,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[improved BI-RRT* algorithm,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[goal-biased strategy,](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)[refined artificial potential field method](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)\n\n**中图分类号:**\n\n*   [S966.4](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)\n\n*   [TP242.3](https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202509020#)","智慧农业(中英文) 2026, 8(2): 251-264","2026-08-03T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,14,7,1,"针对黄鳝养殖投喂机械臂的路径规划算法改进，方法新颖，数据详实，属于细分行业进展，但影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30,31,32,33],"智慧农业","路径规划","上海市农科院","BI-RRT*","黄鳝投喂","机械臂","上海工程技术大学",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,53],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":52},12,true,[27],[27],[27],{"code":54,"score":22,"supported":14,"title":55,"abstract":56,"tags":57},"F5",[],[],[28],[59,60],"上海工程技术大学 上海市农科院 智慧农业 路径规划","上海工程技术大学 上海市农科院","上海工程技术大学上海市农科院智慧农业路径规划-246",6,"10.12133\u002Fj.smartag.sa202509020","2026-08-05T23:53:02.427048Z",{"total":62,"page":22,"page_size":62,"items":66},[67,126,184,230,270,329],{"id":68,"title":69,"url":70,"summary":71,"summary_zh":8,"content":8,"source_name":72,"source_url":70,"published_at":73,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":79,"tags":81,"primary_topic":86,"secondary_topics":87,"classification":88,"search_phrases":103,"slug":106,"view_count":107,"doi":108,"paper":109,"created_at":125},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":76,"substance":77,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":78},16,21,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[80],{"name":72,"url":70},[27,82,83,84,85],"农业人工智能","精准农业","机器视觉","作物行检测",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":89},{"candidates":90,"agricultural_evidence":49},[91,97],{"code":35,"score":92,"supported":49,"title":93,"abstract":94,"tags":96},5,[],[95],"precision agriculture",[27,83],{"code":98,"score":99,"supported":14,"title":100,"abstract":101,"tags":102},"I2",2,[],[],[82,84],[104,105],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539",0,"10.1007\u002Fs11119-026-10473-z",{"doi":108,"openalex_id":110,"authors":111,"venue":72,"cited_by_count":107,"oa_url":8,"card":8,"direction":8,"ingested_from":124},"W7223117828",[112,115,118,121],{"name":113,"orcid":114},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":116,"orcid":117},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":119,"orcid":120},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":122,"orcid":123},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","openalex","2026-10-22T11:20:09.678587Z",{"id":127,"title":128,"url":129,"summary":130,"summary_zh":131,"content":8,"source_name":132,"source_url":129,"published_at":133,"category":12,"cover_url":8,"hotness":134,"is_selected":14,"score":135,"score_detail":136,"sources":139,"tags":143,"primary_topic":148,"secondary_topics":149,"classification":150,"search_phrases":166,"slug":169,"view_count":107,"doi":170,"paper":171,"created_at":183},5397,"The Relationship Between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23469780","Relationship between Nutrient Concentration and Measurable Optical Characteristics of Banana Leaves: A Quantitative Image-Based Study explores the relationship between nutrient concentrations and the optical characteristics of banana leaves using quantitative image analysis. The study focuses on measurable leaf features, including colour intensity, RGB values, and other image-derived parameters, to identify patterns associated with plant nutrient status. Digital images of banana leaves can provide valuable information about variations in leaf appearance that may reflect differences in nutrient availability. By analysing these characteristics alongside nutrient concentration measurements, the study aims to establish quantitative relationships that can support plant nutrient assessment. This approach may contribute to the development of cost-effective, non-destructive, and image-based techniques for monitoring banana plant health. The findings can be useful for precision agriculture, crop management, and future research on digital image processing for agricultural applications.","香蕉叶片养分浓度与可测量光学特征之间的关系：一项基于定量图像的研究探讨了利用定量图像分析研究香蕉叶片养分浓度与光学特征之间的关系。该研究聚焦于可测量的叶片特征，包括颜色强度、RGB值及其他图像衍生参数，以识别与植物养分状况相关的模式。香蕉叶片的数字图像可提供关于叶片外观变化的有价值信息，这些变化可能反映养分有效性的差异。通过将这些特征与养分浓度测量结果一并分析，该研究旨在建立可支持植物养分评估的定量关系。该方法可能有助于开发低成本、非破坏性且基于图像的香蕉植株健康监测技术。研究结果可用于精准农业、作物管理以及未来农业应用数字图像处理的研究。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",25,66,{"impact":48,"substance":19,"depth":17,"authority":48,"freshness":137,"relevant":22,"comment":138},9,"该研究基于图像定量分析香蕉叶片营养状况，方法新颖且面向无损监测，对精准农业有参考价值，但属单作物探索性研究，影响范围有限。",[140,141],{"name":132,"url":129},{"name":132,"url":142},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470020",[27,144,145,146,147],"图像分析","香蕉","无损监测","作物营养",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":151},{"candidates":152,"agricultural_evidence":49},[153,157,162],{"code":35,"score":21,"supported":49,"title":154,"abstract":155,"tags":156},[],[83,95],[27],{"code":158,"score":22,"supported":14,"title":159,"abstract":160,"tags":161},"B2",[],[],[145],{"code":98,"score":22,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[144],[167,168],"香蕉叶片 营养浓度 图像分析","香蕉 无损监测 精准农业","香蕉叶片营养浓度图像分析-5397","10.5281\u002Fzenodo.23469780",{"doi":170,"openalex_id":172,"authors":173,"venue":132,"cited_by_count":107,"oa_url":129,"card":176,"direction":182,"ingested_from":124},"W7222941290",[174],{"name":175,"orcid":8},"Anantjit Publication",{"tldr":177,"method":178,"finding":179,"direction":180,"opportunity":181},"用定量图像分析探究香蕉叶片养分浓度与可测光学特征的关系。","采集香蕉叶片数字图像，提取颜色强度、RGB值等参数，与养分浓度做定量关联分析。","叶片图像光学特征与养分浓度存在可量化的关联，可支撑无损养分评估。","农业遥感与作物表型","可扩展到多品种、多生育期与田间光照校正，并构建图像-养分反演模型与便携诊断工具。","数字乡村与农业信息化","2026-10-20T23:30:22.619634Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":8,"content":8,"source_name":189,"source_url":187,"published_at":133,"category":12,"cover_url":8,"hotness":134,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":198,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":212,"slug":215,"view_count":107,"doi":216,"paper":217,"created_at":229},5396,"Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22601.71520","Understanding the Digital Agriculture Landscape in Central Africa: 2000-2026 Systematic Review.。OpenAlex","OpenAlex",37,{"impact":192,"substance":62,"depth":192,"authority":62,"freshness":137,"relevant":22,"comment":193},8,"中非数字农业系统综述，主题相关但摘要信息极少，仅题录级元数据，增量与权威性有限，不建议进入每日精选。",[195,196],{"name":189,"url":187},{"name":189,"url":197},"https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.16146.41922",[199,27,200,201],"数字农业","农业信息化","中非",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":205},{"candidates":206,"agricultural_evidence":49},[207],{"code":35,"score":20,"supported":49,"title":208,"abstract":210,"tags":211},[209],"digital agriculture",[209],[200,199,27],[213,214],"中非 数字农业","农业信息化 数字农业 智慧农业 中非","中非数字农业-5396","10.13140\u002Frg.2.2.22601.71520",{"doi":216,"openalex_id":218,"authors":219,"venue":8,"cited_by_count":107,"oa_url":187,"card":8,"direction":182,"ingested_from":124},"W7222930115",[220,222,224,227],{"name":221,"orcid":8},"Anani Ogou",{"name":223,"orcid":8},"Henry Chotangui",{"name":225,"orcid":226},"Honoré Beyegue-Djonko","https:\u002F\u002Forcid.org\u002F0009-0004-6447-5889",{"name":228,"orcid":8},"Christopher Mubeteneh Tankou","2026-10-20T23:30:22.542913Z",{"id":231,"title":232,"url":233,"summary":234,"summary_zh":8,"content":8,"source_name":189,"source_url":233,"published_at":133,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":238,"tags":240,"primary_topic":243,"secondary_topics":244,"classification":245,"search_phrases":258,"slug":261,"view_count":107,"doi":262,"paper":263,"created_at":269},5380,"Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.17987.98089","Automation, Robotics, and Precision Agriculture Transformation Through Smart Harvest Systems。OpenAlex",56,{"impact":19,"substance":192,"depth":13,"authority":48,"freshness":192,"relevant":22,"comment":237},"主题契合智慧农业，但摘要仅为标题重复、缺乏方法与数据细节，信息增量有限，暂不建议进入每日精选。",[239],{"name":189,"url":233},[27,241,83,242],"农业机器人","智能采收",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":246},{"candidates":247,"agricultural_evidence":49},[248,253],{"code":35,"score":249,"supported":49,"title":250,"abstract":251,"tags":252},13,[95],[95],[27,83],{"code":254,"score":22,"supported":14,"title":255,"abstract":256,"tags":257},"F6",[],[],[241],[259,260],"农业机器人 智慧农业 智能采收 精准农业","农业机器人 智慧农业","农业机器人智慧农业智能采收精准农业-5380","10.13140\u002Frg.2.2.17987.98089",{"doi":262,"openalex_id":264,"authors":265,"venue":8,"cited_by_count":107,"oa_url":233,"card":8,"direction":268,"ingested_from":124},"W7222971426",[266],{"name":267,"orcid":8},"Joydeep Dey","智慧农业 \u002F 农业物联网","2026-10-20T23:30:08.600540Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":8,"source_name":276,"source_url":273,"published_at":133,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":281,"tags":283,"primary_topic":288,"secondary_topics":289,"classification":290,"search_phrases":298,"slug":301,"view_count":107,"doi":302,"paper":303,"created_at":328},5375,"Triboelectric Nanogenerator Based on All‐Cellulose Triboelectric Layer for Droplet Energy Harvesting and Self‐Powered Agriculture Monitoring","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsmll.76354","Cellulose-based triboelectric nanogenerators (TENGs) attract increasing attention; however, developing high-performance all-cellulose-based triboelectric layer TENGs remains challenging. In this study, a regenerated cellulose nanofiber film (R-CNF) is used as a substrate, and an aminosilane intermediate layer is introduced. By utilizing the silanol groups on the silane chains as compatible reactive sites for fluorination grafting, and simultaneously enhancing the bonding strength between modified layers through N-H···F-C hydrogen-bond interactions between amino and fluorine groups, a fluorinated regenerated cellulose nanofiber film (F-R-CNF) is successfully prepared. The degradation rate of F-R-CNF within 13 days is approximately 0.96%. When assembled into a TENG with an ethyl cellulose nanofiber film(ECNF), the output voltage of the resulting TENG is ten times higher than that of the R-CNF-based TENGs, while maintaining 85% of its output performance at 80% relative humidity. Furthermore, a droplet energy harvesting TENG (DE-TENG) is constructed to harvest energy from falling water droplets, demonstrating the potential of the DE-TENG to power agricultural micro-sensors. This study provides a feasible strategy for designing high-performance all-cellulose-based triboelectric layer triboelectric nanogenerators and reveals the great potential of F-R-CNF for self-powered devices and smart agriculture applications.","纤维素基摩擦纳米发电机（TENGs）日益受到关注，然而开发高性能全纤维素基摩擦电层TENG仍面临挑战。本研究以再生纤维素纳米纤维膜（R-CNF）为基底，引入氨基硅烷中间层，利用硅烷链上的硅醇基团作为氟化接枝的相容反应位点，同时通过氨基与含氟基团之间的N-H···F-C氢键相互作用增强改性层间的结合强度，成功制备了氟化再生纤维素纳米纤维膜（F-R-CNF）。F-R-CNF在13天内的降解率约为0.96%。将其与乙基纤维素纳米纤维膜（ECNF）组装成TENG后，所得TENG的输出电压比基于R-CNF的TENG高十倍，同时在80%相对湿度下仍保持85%的输出性能。此外，构建了液滴能量收集TENG（DE-TENG）以收集下落水滴的能量，展示了DE-TENG为农业微传感器供电的潜力。本研究为设计高性能全纤维素基摩擦电层摩擦纳米发电机提供了可行策略，并揭示了F-R-CNF在自供电器件和智慧农业应用中的巨大潜力。","Small",81,{"impact":19,"substance":18,"depth":279,"authority":20,"freshness":192,"relevant":22,"comment":280},19,"全纤维素摩擦纳米发电机实现液滴能量收集并驱动农业微传感器，方法新颖、数据扎实，属智慧农业自供电传感的实质进展，值得入选。",[282],{"name":276,"url":273},[27,284,285,286,287],"摩擦纳米发电","液滴能量收集","自供电传感","纤维素材料",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":291},{"candidates":292,"agricultural_evidence":49},[293],{"code":35,"score":21,"supported":49,"title":294,"abstract":295,"tags":297},[],[27,296],"smart agriculture",[27],[299,300],"全纤维素 摩擦纳米发电机 液滴能量收集","F-R-CNF 自供电 农业监测","全纤维素摩擦纳米发电机液滴能量收集-5375","10.1002\u002Fsmll.76354",{"doi":302,"openalex_id":304,"authors":305,"venue":276,"cited_by_count":107,"oa_url":8,"card":323,"direction":268,"ingested_from":124},"W7222917387",[306,308,311,313,315,318,320],{"name":307,"orcid":8},"Hengwei Xiong",{"name":309,"orcid":310},"Qiaonan Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9365-0867",{"name":312,"orcid":8},"Weijie Li",{"name":314,"orcid":8},"Kangwen Shi",{"name":316,"orcid":317},"Xinyan Sun","https:\u002F\u002Forcid.org\u002F0009-0008-6901-8977",{"name":319,"orcid":8},"Wenpeng Fan",{"name":321,"orcid":322},"Youqiang Zhang","https:\u002F\u002Forcid.org\u002F0009-0006-9129-8050",{"tldr":324,"method":325,"finding":326,"direction":268,"opportunity":327},"制备全纤维素摩擦电层TENG，收集水滴能量为农业微传感器供电。","再生纤维素纳米纤维膜经氨基硅烷中间层氟化接枝，与乙基纤维素膜组装TENG。","输出达R-CNF基TENG的10倍，80%湿度下保持85%性能，13天降解约0.96%。","可探索全纤维素TENG在田间高湿环境下的长期稳定性及与无线传感节点的集成供电。","2026-10-20T23:30:08.252085Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":8,"source_name":132,"source_url":332,"published_at":133,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":74,"score_detail":335,"sources":337,"tags":339,"primary_topic":342,"secondary_topics":343,"classification":344,"search_phrases":357,"slug":360,"view_count":107,"doi":361,"paper":362,"created_at":372},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。",{"impact":19,"substance":18,"depth":19,"authority":48,"freshness":137,"relevant":22,"comment":336},"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[338],{"name":132,"url":332},[27,82,340,83,341],"数字孪生","植物表型",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":345},{"candidates":346,"agricultural_evidence":49},[347,353],{"code":35,"score":348,"supported":49,"title":349,"abstract":351,"tags":352},20,[350],"digital twin",[83,340,95],[340,27,83],{"code":98,"score":22,"supported":14,"title":354,"abstract":355,"tags":356},[],[],[82],[358,359],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":361,"openalex_id":363,"authors":364,"venue":132,"cited_by_count":107,"oa_url":332,"card":367,"direction":180,"ingested_from":124},"W7222885652",[365],{"name":366,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":368,"method":369,"finding":370,"direction":180,"opportunity":371},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z"]