**6. Conclusions**

Most classical statistical methods use only simple statistics and few influential factors to assess the biological features of plants. For example, Y<sup>p</sup> and Y<sup>s</sup> are the only indices used to identify drought-tolerant plant genotypes in yield-based drought tolerance assessment methods. However, there are other influential factors, such as cellular, physiological, and phytochemical pathways, which are involved in plants' responses to environmental stress. The tolerance of different plant species to biotic and abiotic stresses, as complex biological processes, can be efficiently enhanced through large-scale analysis of phenomic, metabolomic, and genomic data. Machine learning models are capable of processing large amounts of data (imaging and remote-sensing data) for high-throughput stress phenotyping. The analysis of different omics and phenomic data may result in more precise interpretation of GEI and yield stability. Plants' qualitative and quantitative characteristics can be predicted more precisely by analysis of climate data (temperature, humidity, sunshine, precipitation, etc.), soil factors, agricultural operations data (harvest date, information on diseases, crop status, ground temperature, etc.), topographic, and meteorological data. Big data analysis enables more efficient classification of plants' phenotypes and genotypes. Machine learning techniques are able to manage large amounts of data in various areas of plant breeding, which can lead to more accurate results and better interoperation than classical statistical methods. Artificial neural networks can be used for pattern recognition, nonlinear regression, and classification purposes in plant tissue culture studies because they can handle binary, continuous, categorical, and fuzzy datasets. The present review can give an overview of applications of machine learning to plant breeders. It would be helpful to adopt the correct method of data analysis in future studies, which in turn can increase the output of studies.

**Author Contributions:** M.N. and G.N. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The publication was co-financed within the framework of the Ministry of Science and Higher Education program titled "Regional Initiative Excellence" in 2019–2022, project no. 005/RID/2018/19.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


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