[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2520":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":66},2520,"Exemples d'apports de l'intelligence artificielle (IA) pour l'envirotypage et Phénotypage numérique des plantes","https:\u002F\u002Fdoi.org\u002F10.17180\u002Fciag-2026-vol114-art03","The characterization of plants and their environment is changing scale with the development of digital plant phenotyping tools and methods. Phenotyping and envirotyping tools are more accessible, enabling multi-scale observation of plants and their environment—even molecules—through multi-plot observation via satellite. At the same time, large amounts of data are more accessible and easier to exploit through indexing in open information systems. Processing is facilitated by an increasing number of sophisticated tools, particularly with the help of artificial intelligence, thus facilitating analysis by the operator. Two applicated examples are detailled here regarding segmentation for combined mixed crops and insects identification and counting. Environmental and phenotypic data can be combined in predictive or decision support models. These data are valuable assets for accelerating the deployment of agroecology and resilient, sustainable agriculture.","随着数字植物表型分析工具和方法的发展，植物及其环境的表征正在发生尺度上的变化。表型分析和环境型分析工具变得更加普及，使得通过卫星进行多地块观测，实现对植物及其环境——甚至分子——的多尺度观察成为可能。与此同时，大量数据更易获取，并且通过开放信息系统中的索引编制更易于利用。越来越多的复杂工具促进了数据处理，尤其是在人工智能的帮助下，从而便于操作人员进行分析。本文详细介绍了两个应用实例，分别涉及混合种植作物的分割以及昆虫的识别与计数。环境和表型数据可以结合到预测模型或决策支持模型中。这些数据是加速部署生态农业以及具有韧性的可持续农业的宝贵资产。",null,"INRAE","2026-09-14T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,15,8,1,"INRAE 发布数字表型与环境型技术应用论文，展示 AI 在混合种植分割和昆虫识别计数中的落地案例，方法新颖、数据规模可观，对智慧农业与育种数字化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","遥感监测","作物育种","数字表型",0,"10.17180\u002Fciag-2026-vol114-art03",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":59,"direction":63,"ingested_from":65},"W7213212183",[37,39,42,45,47,49,51,53,55,57],{"name":38,"orcid":9},"Benoit DE SELON",{"name":40,"orcid":41},"Jean‐Eudes Hollebecq","https:\u002F\u002Forcid.org\u002F0000-0002-5089-9179",{"name":43,"orcid":44},"Jordan Bernigaud-Samatan","https:\u002F\u002Forcid.org\u002F0009-0006-2624-008X",{"name":46,"orcid":9},"Caroline Chaynes",{"name":48,"orcid":9},"Solenne Faul-Godec",{"name":50,"orcid":9},"Mathieu MARGUERIE",{"name":52,"orcid":9},"Afonso Ponce",{"name":54,"orcid":9},"Mario Serouart",{"name":56,"orcid":9},"Samuel Thomas",{"name":58,"orcid":9},"Tania ROUGIER",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"综述人工智能在植物环境型与数字表型中的应用，并给出混合种植分割和昆虫计数两个实例。","数字表型与环境型工具、卫星多尺度观测、开放信息系统及AI图像分割与识别。","AI可提升植物与环境数据的分割、昆虫识别计数效率，并支撑预测与决策模型。","农业遥感与作物表型","可探索多尺度表型与环境数据融合的轻量AI模型，用于混合种植和病虫害实时决策。","openalex","2026-09-15T23:30:15.799807Z"]