Artificial intelligence and machine learning applications in modern plant breeding and genomic innovation

Crop breeding and agricultural research has improved significantly in recent years due to the quick development of new technology. Modern breeding is different from traditional breeding due to advances in technical tools and breeding concepts. The main objective of current plant breeding is to apply cutting-edge technology to increase crop productivity, whereas traditional breeding initially concentrated on high yield. Modern plant breeding programs integrate emerging technology particularly artificial intelligence (AI) and machine learning (ML) to enhance crop productivity. Artificial intelligence (AI) has drawn a lot of attention due to its potent capabilities in big data analysis and pattern recognition. AI is revolutionizing many tools in different aspects of plant breeding, such as data collection, exploring genetic diversity within gene banks, and bridging the gap between genotype and phenotype to accelerate crop improvement. The methods of modern breeding begin with the development of diverse genetic material populations. Furthermore, gene function can be studied at multiple regulatory levels, including transcriptional, post-transcriptional, and translational levels, as well as through post-translational modifications. By increasing the accuracy of gene editing systems and forecasting the possible consequences of gene variations on plant phenotypes, AI-based techniques also show promising results for enhancing crop qualities. This review articles aims to explore the use of these contemporary scientific and technological methods in crop breeding. Furthermore, the advancement of precise gene-editing technologies, combined with AI-driven analytical tools, is expected to significantly impact the future of modern plant breeding.