机器学习方法在植物表型分析中的应用研究现状

打开文本图片集

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]。（剩余34690字）