Opportunities and Prospects of Artificial Intelligence in Plant Genomics

The global food production system is currently impacted by several challenges, and hence, the adoption of modern technologies for crop improvement and sustainable agriculture is the need of the hour. The realm of omic technologies has revolutionized agricultural research by exploring high throughput genomic and transcriptomic data to identify suitable candidate genes for developing favorable agronomic and physiological traits. With a rapid burst of “biological big data”, the use of artificial intelligence methods has been vital in extracting remarkable patterns contributing to predictions of biomarker genes from genomic data, genomic selection (GS) and marker-assisted selection (MAS), gene structure and regulatory element prediction and also in processes like host–pathogen interaction prediction. We review the prospects of machine learning techniques in dealing with complex biological datasets with predictive potential in various fields, including plant genomics, as well as the successful applications thereof.