[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2289":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":62},2289,"High-throughput phenotyping platform for facility crops based on optical sensing technology: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102545","High-throughput acquisition of crop phenotypic information is one of the key technologies for achieving intelligent facility agriculture and precision breeding. Traditional phenotypic data collection methods suffer from low efficiency and strong subjectivity, making it difficult to achieve multi-scale continuous monitoring and meet the demands of modern research and production. This paper systematically reviews the technological framework and development trajectory of optical sensing technology-driven phenotypic platforms for facility crops. First, starting from optical sensing technologies, a comparative analysis highlights the advantages and limitations of RGB, multi-\u002Fhyperspectral, thermal infrared, and LiDAR sensors in phenotypic perception. Second, the characteristics and applicable scenarios of stationary, rail-mounted, mobile robot, and unmanned aerial vehicle (UAV) platform architectures are summarized. Furthermore, the evolution of phenotypic data processing methods is examined, focusing on the shift from traditional feature engineering to deep learning-driven approaches. Finally, key challenges such as multimodal data fusion, system cost, and real-time performance are discussed, along with the future direction of phenotypic platforms toward intelligent closed-loop decision-making systems. This article systematically reviews the facility agriculture phenotyping platforms driven by optical sensing technology, and also incorporates representative research progress in field phenotyping studies. These advances provide transferable sensing technologies, methodological frameworks, and platform design concepts that can facilitate the development of phenotyping platforms for controlled-environment agriculture.","高通量获取作物表型信息是实现智能设施农业和精准育种的关键技术之一。传统表型数据采集方式效率低、主观性强，难以实现多尺度连续监测，无法满足现代研究与生产需求。本文系统综述了光学传感技术驱动的设施作物表型平台的技术框架与发展脉络。首先，从光学传感技术出发，对比分析了RGB、多光谱\u002F高光谱、热红外和激光雷达传感器在表型感知中的优势与局限。其次，总结了固定式、轨道式、移动机器人和无人机平台架构的特点与适用场景。进而，梳理了表型数据处理方法的演进，重点分析了从传统特征工程到深度学习驱动方法的转变。最后，讨论了多模态数据融合、系统成本和实时性等关键挑战，并展望了表型平台向智能闭环决策系统发展的未来方向。本文系统综述了光学传感技术驱动的设施农业表型平台，同时纳入了田间表型研究中的代表性进展。这些进展提供了可迁移的传感技术、方法框架和平台设计理念，有助于推动受控环境农业表型平台的发展。",null,"Smart Agricultural Technology","2026-09-10T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,6,1,"系统综述光学传感驱动的设施作物高通量表型平台，涵盖传感器对比、平台架构与深度学习数据处理演进，方法框架清晰、可迁移性强，对智慧设施农业与精准育种有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","设施农业","高通量表型","光学传感",0,"10.1016\u002Fj.atech.2026.102545",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":54,"card":55,"direction":59,"ingested_from":61},"W7212130452",[37,39,41,43,45,47,50,52],{"name":38,"orcid":9},"Xiaodong Zhang",{"name":40,"orcid":9},"Zhaowei Li",{"name":42,"orcid":9},"Yi Zhang",{"name":44,"orcid":9},"Chuandong Guo",{"name":46,"orcid":9},"Zonghua Leng",{"name":48,"orcid":49},"Xiangyu Han","https:\u002F\u002Forcid.org\u002F0000-0003-0412-9859",{"name":51,"orcid":9},"Hanping Mao",{"name":53,"orcid":9},"Yixue Zhang","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007707\u002Fpdf",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"综述光学传感驱动的设施作物高通量表型平台，涵盖传感器、平台架构与数据处理。","综述RGB、多\u002F高光谱、热红外、LiDAR及固定\u002F轨道\u002F机器人\u002F无人机平台与深度","表型平台正从传统特征工程转向深度学习，并迈向智能闭环决策系统。","农业遥感与作物表型","多模态数据融合、低成本实时表型平台及闭环决策在设施农业中仍待突破。","openalex","2026-09-13T23:30:04.259059Z"]