[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3965":3,"related-3965":90},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":89},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。",null,"Computers and Electronics in Agriculture","2026-10-01T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业机器人","果园自动化","苹果采摘","柔性夹爪",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,58],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":52},17,true,[48],"apple",[50,51,48],"苹果","orchard",[],{"code":54,"score":21,"supported":14,"title":55,"abstract":56,"tags":57},"F6",[],[],[27],{"code":59,"score":21,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[26],[64,65],"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",3,"10.1016\u002Fj.compag.2026.112477",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7215051321",[72,75,78],{"name":73,"orcid":74},"Ali Yasar","https:\u002F\u002Forcid.org\u002F0000-0003-0114-4471",{"name":76,"orcid":77},"Dilshan Ranasinghe","https:\u002F\u002Forcid.org\u002F0009-0000-5293-8584",{"name":79,"orcid":80},"Chao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1201-2039",0,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"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","农业人工智能与决策模型","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。","openalex","2026-10-02T23:30:01.662869Z",{"total":91,"page":21,"page_size":91,"items":92},6,[93,158,239,285,349,401],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":106,"tags":108,"primary_topic":111,"secondary_topics":112,"classification":113,"search_phrases":134,"slug":137,"view_count":138,"doi":139,"paper":140,"created_at":157},3780,"Epistemological Friction: Embodied Expertise and the ‘Last Mile’ of Agricultural Automation in the US Tree‐Fruit Industry","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fntwe.70047","ABSTRACT Autonomous and AI‐driven agricultural technology is often framed as a panacea for labour dependency. While proponents envision autonomous technologies seamlessly optimising farming, this rationalised model misunderstands the reality of on‐farm integration. Based on an ethnographic and comparative study of 16 operations in the US West Coast Tree Fruit Industry (California and Washington), this article examines the ‘last mile’ of agricultural automation. The study reveals that integrating AI and robotics is a contested negotiation shaped by epistemological friction—the tension between the algorithmic logic of developers and the embodied, site‐specific expertise of farmers and farm workers. Rather than resulting in seamless labour replacement, we find that agricultural automation reconfigures work, creating an invisible reliance on ‘human middleware’ to navigate the biological recalcitrance of the orchard. Demonstrating how regional contexts and participatory implementation shape technological trajectories, this study extends Labour Process Theory. We argue for the need for a relational approach recognising the essential cognitive labour of farmers and farm workers that is demanded by digital agriculture. Ultimately, this study shows the ‘farm of the future’ depends on reconciling rigid systems with the situated expertise of the farm workforce.","自主与人工智能驱动的农业技术常被描绘为解决劳动力依赖问题的万能药。支持者设想自主技术能够无缝优化农业生产，然而这种理性化模型误解了农场整合的现实。本文基于对美国西海岸果树产业（加利福尼亚州和华盛顿州）16家经营主体的民族志比较研究，考察农业自动化的“最后一英里”。研究发现，人工智能与机器人技术的整合是一场充满争议的协商过程，其受到认识论摩擦的塑造——即开发者的算法逻辑与农民及农场工人具身的、场所特定的专业知识之间的张力。我们发现，农业自动化并未实现无缝的劳动力替代，而是重构了工作形态，制造了对“人类中间件”的隐性依赖，以应对果园的生物性阻力。本研究通过展示区域情境与参与式实施如何塑造技术轨迹，拓展了劳动过程理论。我们主张需要一种关系性方法，承认数字农业所要求的农民和农场工人不可或缺的认知劳动。最终，本研究表明“未来农场”取决于刚性系统与农场劳动力情境化专业知识之间的调和。","New Technology Work and Employment","2026-09-27T00:00:00Z",80,{"impact":17,"substance":103,"depth":17,"authority":19,"freshness":104,"relevant":21,"comment":105},22,8,"基于美西16家果园民族志研究，揭示AI与机器人落地的“最后一公里”认识论摩擦与“人类中间件”隐性依赖，对智慧农业人机协同具参考价值。",[107],{"name":99,"url":96},[109,26,110,27,28],"数字农业","农业人工智能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":114},{"candidates":115,"agricultural_evidence":46},[116,121,125,129],{"code":59,"score":104,"supported":46,"title":117,"abstract":118,"tags":120},[],[109,119],"digital agriculture",[109,26],{"code":32,"score":91,"supported":46,"title":122,"abstract":123,"tags":124},[],[34,51],[],{"code":54,"score":21,"supported":14,"title":126,"abstract":127,"tags":128},[],[],[27],{"code":130,"score":21,"supported":14,"title":131,"abstract":132,"tags":133},"I2",[],[],[110],[135,136],"美国 果树 自动化","果园 机器人 劳动力","美国果树自动化-3780",4,"10.1111\u002Fntwe.70047",{"doi":139,"openalex_id":141,"authors":142,"venue":99,"cited_by_count":81,"oa_url":149,"card":150,"direction":156,"ingested_from":88},"W7214618881",[143,146],{"name":144,"orcid":145},"M. Anne Visser","https:\u002F\u002Forcid.org\u002F0000-0001-5047-7702",{"name":147,"orcid":148},"Alex Bladimir Hernandez","https:\u002F\u002Forcid.org\u002F0009-0001-5320-7203","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fntwe.70047",{"tldr":151,"method":152,"finding":153,"direction":154,"opportunity":155},"通过美西果树业民族志研究，揭示农业自动化落地中算法逻辑与农户经验知识的冲突。","对加州和华盛顿16个果园开展民族志与比较研究。","自动化未无缝替代劳动，反而依赖‘人类中间件’协调生物不确定性。","数字乡村与农业信息化","可探索参与式设计如何将农户隐性知识嵌入AI系统，弥合‘最后一英里’鸿沟。","智慧农业 \u002F 农业物联网","2026-09-29T23:30:12.281947Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":163,"content":9,"source_name":164,"source_url":161,"published_at":165,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":176,"secondary_topics":177,"classification":178,"search_phrases":202,"slug":205,"view_count":21,"doi":206,"paper":207,"created_at":238},4889,"A Distributed Multirobot Architecture for Decoupled Inspection and Localized Treatment in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Faisy.70593","This work presents a distributed multirobot architecture for autonomous inspection and precision treatment in agriculture, validated under real vineyard conditions. The proposed system integrates a heterogeneous fleet of four specialized ground robots (two inspection and two treatment platforms), coordinated through a cloud‐based mission control center (MCC). The MCC acts as the cognitive layer of the system, integrating CNN‐based disease detection, mission planning, and fleet management, while the robotic platforms operate as autonomous agents responsible for sensing and actuation. Inspection and treatment processes are separated through an event‐driven, grid‐based coordination mechanism maintained by the MCC, enabling asynchronous and independent operation of both robot types without direct inter‐robot communication. Georeferenced images acquired by the inspection robots are transmitted in real time to the MCC, where the disease detection model identifies affected areas and updates the treatment grid accordingly. Experimental validation in a commercial vineyard demonstrated the feasibility of the approach: the model achieved a recall of 95.2% against expert annotations at a 16% infection prevalence, no data losses were recorded across communication streams, and 21.1% of acquired images generated treatment events. The proposed architecture is not tied to a specific task and can be extended to other precision agriculture scenarios.","本文提出了一种用于农业自主巡检与精准处理的分布式多机器人架构，并在真实葡萄园条件下进行了验证。所提出的系统集成了由四台专用地面机器人（两台巡检平台和两台处理平台）组成的异构机器人集群，并通过基于云的任务控制中心（MCC）进行协调。MCC作为系统的认知层，集成了基于CNN的病害检测、任务规划和集群管理，而机器人平台则作为负责感知与执行的自主智能体。巡检与处理过程通过MCC维护的事件驱动、基于网格的协调机制实现分离，使两类机器人能够异步独立运行，无需机器人之间的直接通信。巡检机器人采集的地理参考图像实时传输至MCC，由病害检测模型识别受影响区域并相应更新处理网格。在商业葡萄园中的实验验证表明了该方法的可行性：在16%感染率下，模型相对于专家标注达到了95.2%的召回率，各通信流中未记录到数据丢失，21.1%的采集图像触发了处理事件。所提出的架构不局限于特定任务，可扩展至其他精准农业场景。","Advanced Intelligent Systems","2026-10-12T00:00:00Z",79,{"impact":17,"substance":103,"depth":17,"authority":168,"freshness":104,"relevant":21,"comment":169},13,"多机器人分布式架构在真实葡萄园验证，检出召回率95.2%，对精准农业机器人协同有参考价值。",[171],{"name":164,"url":161},[26,27,173,174,175],"精准施药","病害检测","多机器人协同",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":179},{"candidates":180,"agricultural_evidence":46},[181,188,193,198],{"code":59,"score":182,"supported":46,"title":183,"abstract":185,"tags":187},15,[184],"precision agriculture",[186,184],"精准农业",[26],{"code":32,"score":67,"supported":46,"title":189,"abstract":190,"tags":192},[],[191],"葡萄",[],{"code":194,"score":21,"supported":14,"title":195,"abstract":196,"tags":197},"F4",[],[],[173],{"code":54,"score":21,"supported":14,"title":199,"abstract":200,"tags":201},[],[],[27],[203,204],"多机器人 葡萄园 病害检测","精准农业 机器人 架构","多机器人葡萄园病害检测-4889","10.1002\u002Faisy.70593",{"doi":206,"openalex_id":208,"authors":209,"venue":164,"cited_by_count":81,"oa_url":232,"card":233,"direction":156,"ingested_from":88},"W7221635124",[210,213,216,219,222,224,226,229],{"name":211,"orcid":212},"Jose Maria Bengochea-Guevara","https:\u002F\u002Forcid.org\u002F0000-0003-4081-7325",{"name":214,"orcid":215},"Héctor Montes","https:\u002F\u002Forcid.org\u002F0000-0001-8638-9966",{"name":217,"orcid":218},"María Nuria Conejero","https:\u002F\u002Forcid.org\u002F0000-0002-9372-3739",{"name":220,"orcid":221},"Laura Garrido-Rey","https:\u002F\u002Forcid.org\u002F0009-0002-1782-5175",{"name":223,"orcid":9},"Carlos Ranz",{"name":225,"orcid":9},"Angelo Vincenzo Barra",{"name":227,"orcid":228},"Dionisio Andújar","https:\u002F\u002Forcid.org\u002F0000-0002-5801-0944",{"name":230,"orcid":231},"Ángela Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0001-5807-8132","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Faisy.70593",{"tldr":234,"method":235,"finding":236,"direction":156,"opportunity":237},"提出分布式多机器人架构，在葡萄园实现异步巡检与精准治疗。","四台异构地面机器人、云任务控制中心、CNN病害检测与网格协调。","病害检测召回率95.2%，通信无数据丢失，21.1%图像触发治疗事件。","可探索去中心化协同、多作物迁移及边缘计算下的实时病害决策优化。","2026-10-14T23:30:13.144306Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":9,"content":9,"source_name":244,"source_url":9,"published_at":245,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":246,"sources":248,"tags":250,"primary_topic":253,"secondary_topics":254,"classification":255,"search_phrases":271,"slug":274,"view_count":275,"doi":9,"paper":276,"created_at":284},3253,"Visuomotor Robotic Pruning in Planar Orchills Using Hybrid Reinforcement Learning（基于混合强化学习的平面果园视觉运动机器人修剪）","https:\u002F\u002Farxivtldr.org\u002Fabs\u002F2609.24906","arXiv发表研究：休眠期树木修剪是劳动密集型但对维持现代高产果园至关重要的工作。本文针对现代平面树形训练系统（V型棚架苹果、UFO樱桃）的修剪任务，提出端到端管线学习闭环视觉运动控制器。控制器完全使用仿真和合成生成数据进行训练，以零样本方式部署到真实果园。管线包括平面果园树网格的合成生成、基于物理的果园仿真器构建、通过运动规划自动收集成功修剪轨迹，以及结合离线演示与在线仿真推出相结合的新型混合强化学习算法。控制器使用腕部相机的光流输入，避免完整三维重建需求，连续引导切割器穿越杂乱分支环境到指定切割点并在真实果园38次物理试验中展示零样本仿真到真实迁移。在V-Trellis苹果上达到49.9%成功率，UFO樱桃上46.0%。","arXiv (Abhinav Jain, Cindy Grimm, Stefan Lee)","2026-09-20T00:00:00Z",{"impact":17,"substance":103,"depth":17,"authority":168,"freshness":20,"relevant":21,"comment":247},"仿真到真实零样本迁移的果园修剪机器人研究，方法新颖、数据扎实，对智慧果园机械化有参考价值。",[249],{"name":244,"url":242},[26,110,27,251,252],"强化学习","果园管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":256},{"candidates":257,"agricultural_evidence":46},[258,262,267],{"code":32,"score":67,"supported":46,"title":259,"abstract":260,"tags":261},[],[50],[],{"code":130,"score":263,"supported":14,"title":264,"abstract":265,"tags":266},2,[],[],[110,251],{"code":59,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[26],[272,273],"V-Trellis 苹果 机器人修剪","UFO 樱桃 零样本迁移","V-Trellis苹果机器人修剪-3253",5,{"doi":9,"openalex_id":9,"authors":277,"venue":9,"cited_by_count":81,"oa_url":9,"card":278,"direction":86,"ingested_from":283},[],{"tldr":279,"method":280,"finding":281,"direction":86,"opportunity":282},"提出端到端视觉运动控制器，用仿真合成数据训练，零样本迁移到真实果园完成修剪。","合成数据生成、物理仿真器、运动规划收集轨迹、混合强化学习、腕部相机光流。","真实果园38次试验零样本迁移成功，苹果49.9%、樱桃46.0%成功率。","可探索多季节、多树种泛化及真实数据微调，提升复杂冠层下的鲁棒性。","agent","2026-09-23T00:04:33.854148Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":9,"source_name":10,"source_url":288,"published_at":291,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":297,"tags":299,"primary_topic":300,"secondary_topics":301,"classification":302,"search_phrases":317,"slug":320,"view_count":67,"doi":321,"paper":322,"created_at":348},1590,"Semantic risk-aware motion planning for apple young-fruit thinning manipulators","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112374","Semantic risk-aware 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1](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-926864804-2D6BD9D516F00E741B307358F344A4EC\u002F0?fmt=jpg&size=284&h=600&w=900&ppv=1)\n\n值得一提的是，开幕式上，马陆葡萄“未来果园”智能采摘、巡检机器人启用。智能设备的落地，让传统葡萄种植管理模式开始革新。\n\n机器人摘葡萄 单机每小时可采摘250串\n\n马陆葡萄产业底蕴深厚，自1981年起开始种植，至今已有40余年的发展历史。作为上海特色农产品产业标杆，马陆葡萄产业规模、品种体系持续完善。\n\n品种持续迭代的背后，是数字科技的全方位赋能。今年，马陆葡萄持续深耕“未来果园”建设，以数字化、智能化赋能传统农业迭代升级，落地全周期智慧种植管理体系，让现代农业科技扎根葡乡田间。\n\n![Image 2](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-2436894883-DB1C4C496F69FF4EC5A5F2F1174E8F4C\u002F0?fmt=jpg&size=308&h=506&w=900&ppv=1)\n\n智能设备的落地，革新了传统葡萄种植管理模式。其中，葡萄园巡检机器人彻底颠覆传统人工巡查模式，告别“人海战术”，可实现田间全天候无人值守作业，自主完成长势监测、病虫害排查、田间异常预警等核心工作，单日巡检覆盖面积超50亩，巡检效率较人工提升5倍，无论烈日还是深夜均可精准值守，实现果园管理数字化、常态化、精细化。\n\n专为马陆葡萄定制的采摘机器人更是突破行业技术壁垒，融合多模态感知、深度学习算法与柔性控制技术，攻克葡萄柔性抓取、无损切割等核心难题，单串葡萄采摘耗时不足3秒，采摘成功率超90%，单机每小时可采摘250串，支持24小时不间断作业，有效破解采摘季用工短缺、人力成本偏高的行业痛点，让智慧种植落地见效。\n\n与此同时，马陆葡萄产业智能管控体系全面升级，全新上线的机器人管理平台可实现两类机器人的统一调度、实时监控、数据溯源与智能分析。配套打造的“小田管家”AI智能体深度融合数十年本土种植经验，结合气象、土壤、作物生长大数据，自动生成标准化农事方案、用药指导与排产规划，变传统经验种植为数据化、精准化、科学化种植，为葡萄稳产提质、产业增效筑牢数字根基，全力打造上海都市现代农业智慧样板。\n\n![Image 3](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-3474761988-4428796A41BA4333810E641B7FE98838\u002F0?fmt=jpg&size=382&h=600&w=900&ppv=1)\n\n以葡萄为媒 丰富农文旅融合内涵\n\n以葡萄为媒，马陆持续推动农文旅深度融合，开幕式上，马陆葡萄艺术村推介片发布，推介片展现了千亩葡萄园的生态风光、乡村与艺术深度融合的景观，勾勒马陆葡萄艺术村的生态之美、人文之美、创意之美，全方位呈现艺术村集生态观光、采摘体验、艺术研学、休闲度假于一体的复合型文旅目的地魅力，进一步擦亮“葡香”马陆文旅名片。\n\n立足自身全链条产业优势和深厚品牌积淀，马陆葡萄积极打破地域壁垒、赋能区域联动发展。开幕式上，举行推进长三角葡萄产业一体化发展倡议书签字仪式。长三角各地葡萄产业代表共同签约，搭建跨区域产业交流合作平台，推动品种研发、智慧种植、品质管控、品牌运营、文旅融合、市场流通等领域资源互通、优势互补、成果共享，合力规范产业标准、提升产区品质、打造区域特色品牌，共同推动长三角葡萄产业规模化、标准化、品牌化、智能化协同发展，助力长三角乡村产业振兴一体化建设。\n\n![Image 4](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-957210179-0E4267C219D9234A4A33F46D12E8F57A\u002F0?fmt=jpg&size=535&h=600&w=900&ppv=1)\n\n下一步，马陆镇将持续深耕“未来果园”建设，深化品牌标准化体系建设，做强农文旅融合业态，深化长三角产业协同，持续放大国家地理标志、上海品牌双重优势，以特色产业赋能乡村提质增效，持续打造可复制、可推广的乡村振兴马陆样板。\n\n来源：新民晚报 作者：杨洁","新民晚报 · 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