Current Research on Application of Machine Learning Methods in Plant Phenotypic Analysis
Guan Sitong
Zhang Zhaoxu
Lin Yiming
Su Peisen
Huang Siluo
Meng Xianyong
Liu Pingzeng
Yan Jun
Abstract：Plant phenotypes are products of the interaction between genotypes and environment,and are the external manifestation of plant life activities,which cover multiple levels and dimensions of plant character-istics,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 produc-tion.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 applica-tion in the analysis of plant morphological structure,stress resistance and biochemical components,as well as 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 inspira-tion for future research in this field.
Keywords：Plant phenotypic analysisMachine learningCrop improvementYield prediction
Publication Date：2025-06-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：13( 158-170 )