Genomics, Phenomics, and Machine Learning in Transforming Plant Research: Advancements and Challenges

Advances in gene editing and natural genetic variability present significant opportunities to generate novel alleles and select natural sources of genetic variation for horticulture crop improvement. The genetic improvement of crops to enhance their resilience to abiotic stresses and new pests due to climate change is essential for future food security. The field of genomics has made significant strides over the past few decades, enabling us to sequence and analyze entire genomes. However, understanding the complex relationship between genes and their expression in phenotypes - the observable characteristics of an organism - requires a deeper understanding of phenomics. Phenomics seeks to link genetic information with biological processes and environmental factors to better understand complex traits and diseases. Recent breakthroughs in this field include the development of advanced imaging technologies, artificial intelligence algorithms, and large-scale data analysis techniques. These tools have enabled us to explore the relationships between genotype, phenotype, and environment in unprecedented detail. This review explores the importance of understanding the complex relationship between genes and their expression in phenotypes. Integration of genomics with efficient high throughput plant phenotyping as well as the potential of machine learning approaches for genomic and phenomics trait discovery.