[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1590":3,"related-1590":95},{"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":22,"tags":24,"primary_topic":28,"secondary_topics":34,"classification":35,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":94},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 motion planning for apple young-fruit thinning manipulators。Computers and Electronics in Agriculture","苹果疏果机械臂的语义风险感知运动规划。《农业计算机与电子》",null,"Computers and Electronics in Agriculture","2026-09-03T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":18,"authority":17,"freshness":19,"relevant":20,"comment":21},12,15,7,1,"针对苹果疏果机器人的语义风险感知运动规划研究，提升作业安全与效率，细分领域进展。",[23],{"name":10,"url":6},[25,26,27],"智慧农业","农业机器人","果园管理",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[],{"status":36,"locked":14,"version":37,"method":38,"evidence":39},"classified","2026-10-22.2","rules",{"candidates":40,"agricultural_evidence":43},[41,49,54],{"code":29,"score":42,"supported":43,"title":44,"abstract":46,"tags":48},14,true,[45],"apple",[47,45],"苹果",[],{"code":50,"score":20,"supported":14,"title":51,"abstract":52,"tags":53},"F6",[],[],[26],{"code":55,"score":20,"supported":14,"title":56,"abstract":57,"tags":58},"I1",[],[],[25],[60,61],"农业机器人 智慧农业 果园管理","农业机器人 智慧农业","农业机器人智慧农业果园管理-1590",3,"10.1016\u002Fj.compag.2026.112374",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":85,"oa_url":86,"card":87,"direction":91,"ingested_from":93},"W7166343784",[68,70,72,74,76,78,80,83],{"name":69,"orcid":9},"谢胜旺",{"name":71,"orcid":9},"T B Zhang",{"name":73,"orcid":9},"Xiaoning He",{"name":75,"orcid":9},"Yanan Hu",{"name":77,"orcid":9},"Shuqi Shang",{"name":79,"orcid":9},"Z G Zhu",{"name":81,"orcid":82},"Xia Gao","https:\u002F\u002Forcid.org\u002F0000-0001-7139-7883",{"name":84,"orcid":9},"Shengda Li",0,"https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.7009815",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"提出语义风险感知运动规划方法，用于苹果疏果机械臂，提高作业安全与效率。","结合语义信息与风险度量，优化机械臂运动规划算法。","该方法能有效降低机械臂作业风险，提升疏果精度。","农业人工智能与决策模型","可探索将语义风险感知扩展到其他精细农业操作，如采摘、修剪，或结合实时传感器数据。","openalex","2026-09-04T23:30:01.595949Z",{"total":96,"page":20,"page_size":96,"items":97},6,[98,150,231,290,346,405],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":9,"content":9,"source_name":103,"source_url":9,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":112,"tags":114,"primary_topic":117,"secondary_topics":118,"classification":119,"search_phrases":136,"slug":139,"view_count":140,"doi":9,"paper":141,"created_at":149},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",80,{"impact":107,"substance":108,"depth":107,"authority":109,"freshness":110,"relevant":20,"comment":111},18,22,13,9,"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。",[113],{"name":103,"url":101},[25,115,26,116,27],"农业人工智能","强化学习",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":120},{"candidates":121,"agricultural_evidence":43},[122,126,132],{"code":29,"score":63,"supported":43,"title":123,"abstract":124,"tags":125},[],[47],[],{"code":127,"score":128,"supported":14,"title":129,"abstract":130,"tags":131},"I2",2,[],[],[115,116],{"code":55,"score":20,"supported":14,"title":133,"abstract":134,"tags":135},[],[],[25],[137,138],"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":142,"venue":9,"cited_by_count":85,"oa_url":9,"card":143,"direction":91,"ingested_from":148},[],{"tldr":144,"method":145,"finding":146,"direction":91,"opportunity":147},"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":151,"title":152,"url":153,"summary":154,"summary_zh":155,"content":9,"source_name":156,"source_url":153,"published_at":157,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":158,"score_detail":159,"sources":162,"tags":164,"primary_topic":168,"secondary_topics":169,"classification":170,"search_phrases":193,"slug":196,"view_count":20,"doi":197,"paper":198,"created_at":230},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":107,"substance":108,"depth":107,"authority":109,"freshness":160,"relevant":20,"comment":161},8,"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。",[163],{"name":156,"url":153},[25,26,165,166,167],"精准施药","病害检测","多机器人协同",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":171},{"candidates":172,"agricultural_evidence":43},[173,179,184,189],{"code":55,"score":18,"supported":43,"title":174,"abstract":176,"tags":178},[175],"precision agriculture",[177,175],"精准农业",[25],{"code":29,"score":63,"supported":43,"title":180,"abstract":181,"tags":183},[],[182],"葡萄",[],{"code":185,"score":20,"supported":14,"title":186,"abstract":187,"tags":188},"F4",[],[],[165],{"code":50,"score":20,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[26],[194,195],"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":197,"openalex_id":199,"authors":200,"venue":156,"cited_by_count":85,"oa_url":223,"card":224,"direction":228,"ingested_from":93},"W7221635124",[201,204,207,210,213,215,217,220],{"name":202,"orcid":203},"Jose Maria Bengochea-Guevara","https:\u002F\u002Forcid.org\u002F0000-0003-4081-7325",{"name":205,"orcid":206},"Héctor Montes","https:\u002F\u002Forcid.org\u002F0000-0001-8638-9966",{"name":208,"orcid":209},"María Nuria Conejero","https:\u002F\u002Forcid.org\u002F0000-0002-9372-3739",{"name":211,"orcid":212},"Laura Garrido-Rey","https:\u002F\u002Forcid.org\u002F0009-0002-1782-5175",{"name":214,"orcid":9},"Carlos Ranz",{"name":216,"orcid":9},"Angelo Vincenzo Barra",{"name":218,"orcid":219},"Dionisio Andújar","https:\u002F\u002Forcid.org\u002F0000-0002-5801-0944",{"name":221,"orcid":222},"Ángela Ribeiro","https:\u002F\u002Forcid.org\u002F0000-0001-5807-8132","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Faisy.70593",{"tldr":225,"method":226,"finding":227,"direction":228,"opportunity":229},"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。","智慧农业 \u002F 农业物联网","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":232,"title":233,"url":234,"summary":235,"summary_zh":236,"content":9,"source_name":10,"source_url":234,"published_at":237,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":242,"tags":244,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":267,"slug":270,"view_count":63,"doi":271,"paper":272,"created_at":289},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。","2026-10-01T00:00:00Z",82,{"impact":107,"substance":240,"depth":107,"authority":42,"freshness":110,"relevant":20,"comment":241},23,"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。",[243],{"name":10,"url":234},[25,26,245,246,247],"果园自动化","苹果采摘","柔性夹爪",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":251},{"candidates":252,"agricultural_evidence":43},[253,259,263],{"code":29,"score":254,"supported":43,"title":255,"abstract":256,"tags":258},17,[45],[47,257,45],"orchard",[],{"code":50,"score":20,"supported":14,"title":260,"abstract":261,"tags":262},[],[],[26],{"code":55,"score":20,"supported":14,"title":264,"abstract":265,"tags":266},[],[],[25],[268,269],"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":271,"openalex_id":273,"authors":274,"venue":10,"cited_by_count":85,"oa_url":234,"card":284,"direction":91,"ingested_from":93},"W7215051321",[275,278,281],{"name":276,"orcid":277},"Ali Yasar","https:\u002F\u002Forcid.org\u002F0000-0003-0114-4471",{"name":279,"orcid":280},"Dilshan Ranasinghe","https:\u002F\u002Forcid.org\u002F0009-0000-5293-8584",{"name":282,"orcid":283},"Chao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1201-2039",{"tldr":285,"method":286,"finding":287,"direction":91,"opportunity":288},"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":291,"title":292,"url":293,"summary":294,"summary_zh":9,"content":9,"source_name":295,"source_url":293,"published_at":296,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":158,"score_detail":297,"sources":301,"tags":303,"primary_topic":306,"secondary_topics":307,"classification":308,"search_phrases":324,"slug":327,"view_count":63,"doi":328,"paper":329,"created_at":345},3808,"Orchard worker detection using spatial-context attention on 2D LiDAR data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104604","Orchard worker detection using spatial-context attention on 2D LiDAR data。Biosystems Engineering","Biosystems Engineering","2026-09-30T00:00:00Z",{"impact":298,"substance":299,"depth":107,"authority":42,"freshness":13,"relevant":20,"comment":300},16,21,"提出基于二维激光雷达与空间上下文注意力的果园工人检测方法，属农业人工智能与智能农机细分领域的方法创新，具备一定专业参考价值。",[302],{"name":295,"url":293},[25,115,304,27,305],"目标检测","激光雷达",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":309},{"candidates":310,"agricultural_evidence":43},[311,316,320],{"code":29,"score":312,"supported":43,"title":313,"abstract":314,"tags":315},11,[257],[257],[],{"code":127,"score":128,"supported":14,"title":317,"abstract":318,"tags":319},[],[],[115,304],{"code":55,"score":20,"supported":14,"title":321,"abstract":322,"tags":323},[],[],[25],[325,326],"2D LiDAR 果园 工人检测","农业人工智能 智慧农业 果园管理 激光雷达","2DLiDAR果园工人检测-3808","10.1016\u002Fj.biosystemseng.2026.104604",{"doi":328,"openalex_id":330,"authors":331,"venue":295,"cited_by_count":85,"oa_url":293,"card":9,"direction":9,"ingested_from":93},"W7214801786",[332,334,336,339,342],{"name":333,"orcid":9},"Jintack Jeon",{"name":335,"orcid":9},"Huijong Chung",{"name":337,"orcid":338},"Gookhwan Kim","https:\u002F\u002Forcid.org\u002F0000-0002-7278-3476",{"name":340,"orcid":341},"Yong–Joo Kim","https:\u002F\u002Forcid.org\u002F0000-0002-1212-9018",{"name":343,"orcid":344},"Dae-Hyun Lee","https:\u002F\u002Forcid.org\u002F0000-0001-9544-5974","2026-09-30T23:30:05.310491Z",{"id":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":9,"source_name":352,"source_url":349,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":354,"sources":356,"tags":358,"primary_topic":360,"secondary_topics":361,"classification":362,"search_phrases":382,"slug":385,"view_count":386,"doi":387,"paper":388,"created_at":404},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",{"impact":107,"substance":108,"depth":107,"authority":42,"freshness":160,"relevant":20,"comment":355},"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。",[357],{"name":352,"url":349},[359,25,115,26,245],"数字农业",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":14,"version":37,"method":38,"evidence":363},{"candidates":364,"agricultural_evidence":43},[365,370,374,378],{"code":55,"score":160,"supported":43,"title":366,"abstract":367,"tags":369},[],[359,368],"digital agriculture",[359,25],{"code":29,"score":96,"supported":43,"title":371,"abstract":372,"tags":373},[],[31,257],[],{"code":50,"score":20,"supported":14,"title":375,"abstract":376,"tags":377},[],[],[26],{"code":127,"score":20,"supported":14,"title":379,"abstract":380,"tags":381},[],[],[115],[383,384],"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":387,"openalex_id":389,"authors":390,"venue":352,"cited_by_count":85,"oa_url":397,"card":398,"direction":228,"ingested_from":93},"W7214618881",[391,394],{"name":392,"orcid":393},"M. Anne Visser","https:\u002F\u002Forcid.org\u002F0000-0001-5047-7702",{"name":395,"orcid":396},"Alex Bladimir Hernandez","https:\u002F\u002Forcid.org\u002F0009-0001-5320-7203","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1111\u002Fntwe.70047",{"tldr":399,"method":400,"finding":401,"direction":402,"opportunity":403},"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。","2026-09-29T23:30:12.281947Z",{"id":406,"title":407,"url":408,"summary":409,"summary_zh":410,"content":9,"source_name":295,"source_url":408,"published_at":411,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":420,"secondary_topics":421,"classification":433,"search_phrases":461,"slug":464,"view_count":386,"doi":465,"paper":466,"created_at":484},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。","2026-09-26T00:00:00Z",77,{"impact":18,"substance":108,"depth":107,"authority":42,"freshness":160,"relevant":20,"comment":414},"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[416],{"name":295,"url":408},[25,47,418,27,419],"遥感","精准灌溉",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[422,428],{"code":423,"slug":424,"name":425,"path":426,"parent_code":427},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"code":429,"slug":430,"name":431,"path":432,"parent_code":427},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":36,"locked":14,"version":37,"method":38,"evidence":434},{"candidates":435,"agricultural_evidence":43},[436,440,446,452,456],{"code":29,"score":107,"supported":43,"title":437,"abstract":438,"tags":439},[45],[31,47,257,45],[47],{"code":423,"score":42,"supported":43,"title":441,"abstract":443,"tags":445},[442],"drought",[444,442],"干旱",[],{"code":429,"score":13,"supported":43,"title":447,"abstract":448,"tags":451},[],[449,450,430],"灌溉","水资源",[419],{"code":55,"score":20,"supported":14,"title":453,"abstract":454,"tags":455},[],[],[25],{"code":457,"score":20,"supported":14,"title":458,"abstract":459,"tags":460},"I3",[],[],[418],[462,463],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607","10.1016\u002Fj.biosystemseng.2026.104603",{"doi":465,"openalex_id":467,"authors":468,"venue":295,"cited_by_count":85,"oa_url":408,"card":478,"direction":482,"ingested_from":93},"W7214458453",[469,472,475],{"name":470,"orcid":471},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":473,"orcid":474},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":476,"orcid":477},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":479,"method":480,"finding":481,"direction":482,"opportunity":483},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","农业遥感与作物表型","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","2026-09-27T23:30:09.907348Z"]