高通量植物表型技术在大豆育种中的应用  

Application of high-throughput plant phenotyping technology in soybean breeding

作　　者：张杰[1,2] 谭博阳[1,2] 李亚昕 姜鲲鹏 张晶 李艺彤 李婷婷[1,2] 王帅 魏峭嵘[1,2] 杨明亮[1,2] 陈庆山 徐乐[1,2] ZHANG Jie;TAN Boyang;LI Yaxin;JIANG Kunpeng;ZHANG Jing;LI Yitong;LI Tingting;WANG Shuai;WEI Qiaorong;YANG Mingliang;CHEN Qingshan;XU Le(School of Agriculture,Northeast Agricultural University,Harbin 150030,China;The National Key Laboratory of Smart Farm Technology and Systems,Northeast Agricultural University,Harbin 150030,China)

机构地区：[1]东北农业大学农学院,哈尔滨150030 [2]智慧农场技术与系统全国重点实验室(东北农业大学),哈尔滨150030

出　　处：《东北农业大学学报》2025年第1期139-146,154,共9页Journal of Northeast Agricultural University

基　　金：黑龙江省自然科学基金优秀青年项目(YQ2023C004);黑龙江省重点研发计划项目(2022ZX01A23,JD2023GJ01-12,JD2023GJ01-10)。

摘　　要：高通量植物表型新技术是攻克种质资源优异性状高通量精准鉴定难题、发掘创制突破性种质资源的关键,该技术已成为现代农业研究的核心工具,对推动我国农业高质量发展发挥重要作用。文章系统综述该技术在大豆逆境胁迫监测、生理特性评估、产量预测、抗病性筛选方面的研究进展。通过整合多种传感器和成像技术,高通量植物表型技术可非破坏性地采集大规模的植物表型数据,显著提高大豆育种效率和基因型筛选精准度,为大豆育种提供新方法和新途径。展望大豆育种中表型评估的未来方向和发展趋势,指出高通量植物表型技术在大豆育种应用中存在的不足,提出运用人工智能和机器学习技术提升海量表型数据处理标准化的建议。High-throughput plant phenotyping(HTPP)technology is critical for addressing the challenges of high-throughput and precise identification of superior traits in germplasm resources,thereby enabling the discovery and creation of breakthrough germplasm.It has become a core tool in modern agricultural research and plays a critical role in promoting high-quality agricultural development in China.The progress of HTPP technology in monitoring soybean stress responses,evaluating physiological traits,predicting yield,and screening for good disease resistance was systematically reviewed.By integrating multiple sensors and imaging technologies,HTPP technology enabled the non-destructive collection of large-scale plant phenotypic data,significantly improving breeding efficiency and the accuracy of genotype selection in soybeans.Future directions and trends in phenotypic evaluation for soybean breeding,identifying current limitations in the application of HTPP technology and proposing solutions,such as the use of artificial intelligence and machine learning were also discussed,to address the challenges of standardizing the processing of massive phenotypic datasets.

关 键 词：大豆育种 高通量植物表型技术 种质资源评价 作物监测 

分 类 号：S565.1[农业科学—作物学]