中图分类号：S126：Q94 文献标识号：A 文章编号：1001-4942（2025）06-0158-13

AbstractPlant phenotypes are products of the interaction between genotypes and environment，and are theexternal manifestation of plant life activities，which cover multiple levels and dimensions of plant characteristics，including morphology，physiology and biochemistry.Research on plant phenotypes is a key link in breeding，which is of great significance for revealing related mechanisms of plant life activities，cultivating high-yield，high-quality and efficient crop varieties，and realizing precise management of agricultural production.With the development and application of high-throughput plant phenotype collection technology，plant phenotype data present characteristics of high-dimensionality，multi-source， heterogeneity and dynamics， which bring new opportunities and challenges for plant phenotype analysis. Machine learning，as a powerful tool for data mining and knowledge discovery，can extract useful features and patterns from complex phenotype data，providing new ideas and methods for plant phenotype research.This paper systematically reviewed the application and progress of machine learning methods in plant phenotype research，focusing on their application in the analysis of plant morphological structure，stress resistance and biochemical components，as wellas in crop improvement and yield prediction.The problems and future development directions of machine learning methods in plant phenotype research were also discussed，aiming to provide beneficial references and inspiration for future research in this field.

KeywordsPlant phenotypic analysis； Machine learning； Crop improvement； Yield prediction

植物表型是植物在特定环境条件下展现出的形态、生理、生化特征[1]，反映了植物基因图谱的时序三维表达，以及在不同地域和代际间的变化规律[2-3]。植物表型研究为探索植物的生命活动规律、揭示其适应性机制、改良作物产量等性状提供了重要基础[4]。随着高通量表型采集技术的飞速发展，多角度和多层次的植物表型数据不断涌现，为深入挖掘植物“表型-基因型”关系提供了丰富的数据资源。机器学习（machine learming，ML）是一种基于数据驱动的智能计算方法，可以从大量数据中提取有用信息[5]，为植物表型研究提供了新的思路和方法[6]。早在2010年，ML方法就被应用于从复杂的表型数据中筛选出与作物抗逆性、产量构成因子及品质指标相关的关键特征。例如，Steinfath 等[7]利用偏最小二乘（partialleast squares，PLS）法从马铃薯（Solanum tuberos-um）块茎中筛选出果糖、葡萄糖等化合物作为生物标志物。Yang等[8]通过逐步多元回归（step-wisemultipleregression，SMR）和支持向量机（supportvectormachine，SVM）算法处理高光谱（ 350～2500nm ）遥感图像数据，估算水稻（Oryzasativa）的叶面积指数（leafarea index，LAI）、叶片叶绿素密度（green leaf chlorophyll density，GLCD）等参数。此外，随着组学数据的积累，ML与生物信息学方法的结合为探索植物表型与基因组、转录组、代谢组等组学数据之间的关系提供了帮助[。目前机器学习方法在植物表型分析中的应用主要集中在植物形态结构、胁迫抗性、生化组分、作物改良、产量预测等方面。因此，本文总结了近年来ML在这些方面的研究进展，旨在为今后更深入地研究植物生长发育的分子机制、作物基因改良等提供一定的参考。……