摘要
我国农业正处于由传统农业转向现代化农业的关键阶段,智慧农业是现代化农业发展的重要体现,是未来农业发展的必然趋势。智慧农业旨在将物联网、人工智能以及大数据等现代信息技术与传统农业深度结合,使农业生产实现智能化、绿色化、标准化、数字化。植物表型组学是研究植物表现型特征的科学,是智慧农业发展的关键技术之一,其通过采集细胞、器官、组织、植株以及群体各层面的表型数据,从海量数据中提取可重复性高、可信度高的重要性状信息,为基因挖掘、作物育种和农业生产过程精准管理提供数据支持和方法支撑。从表型数据采集、分析以及国内外植物表型分析平台建设方面综述了智慧农业背景下植物表型组学的发展现状,概述了植物表型组学研究在智慧农业生产中的应用,最后对植物表型组学未来发展趋势做出展望。
China’s agriculture is in the key stage of changing from traditional agriculture to modern agriculture.Smart agriculture is an important embodiment of modern agricultural development and an inevitable trend of agricultural development in the future.Smart agriculture aims to deeply combine modern information technologies such as internet of things,artificial intelligence and big data with traditional agriculture to make agricultural production intelligent,green,standardized and digital.Plant phenomics is a science to study the characteristics of plant phenotypes,which is one of the key technologies for the development of smart agriculture.By collecting phenotypic data at all levels of cells,organs,tissues,plants and populations,plant phenomics extracts important trait information with high repeatability and high reliability from massive data,so as to provide data support and method support for gene mining,crop breeding and accurate management of agricultural production process.This paper reviews the development status of plant phenomics under the background of smart agriculture from the aspects of phenotypic data collection and analysis and the construction of plant phenotypic analysis platform at home and abroad,summarizes the application of plant phenomics research in smart agricultural production,and finally looks forward to the future development trend of plant phenomics.
作者
杨文庆
刘天霞
唐兴萍
徐国富
马喆
杨贺凯
吴文斗
YANG Wenqing;LIU Tianxia;TANG Xingping;XU Guofu;MA Zhe;YANG Hekai;WU Wendou(School of Big Data,Yunnan Agricultural University,Kunming 650201,China;College of Food Science and Technology,Yunnan Agricultural University,Kunming 650201,China;College of Mechanical and Electrical Engineering,Yunnan Agricultural University,Kunming 650201,China)
出处
《河南农业科学》
北大核心
2022年第7期1-12,共12页
Journal of Henan Agricultural Sciences
基金
云南省重大科技专项(202002AE090010)
云南省科技厅科技创新项目(A303202105400202)
云南省科技厅乡村振兴发展科技专项(A3032021089)
2021年云南省“三区”科技人才支持项目(A3032021156005)
云财教[2022]52号乡村振兴科技特派员项目(A3032022009)。
关键词
智慧农业
植物表型组学
成像技术
深度学习
农业生产管理
精准育种
Smart agriculture
Plant phenomics
Imaging technology
Deep learning
Agricultural production management
Precision breeding
