機(jī)器學(xué)習(xí)方法在植物表型分析中的應(yīng)用研究現(xiàn)狀

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中圖分類號(hào)：S126：Q94 文獻(xiàn)標(biāo)識(shí)號(hào)：A 文章編號(hào)：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環(huán)境條件下展現(xiàn)出的形態(tài)、生理、生化特征[1]，反映了植物基因圖譜的時(shí)序三維表達(dá)，以及在不同地域和代際間的變化規(guī)律[2-3]。（剩余34690字）