Abstract— Plant breeding plays a vital role in meeting the needs of ever-increasing global food demands, climate change and sustainable agricultural practices. Artificial Intelligence and Machine Learning algorithms are used in plant breeding for several activities, including genotype-phenotype prediction, genomic selection, trait discovery, and the optimization of breeding methods. These methods help to determine the location of genetic markers that are related to certain traits based on the analysis of big data sets containing genomic and phenotypic information, which in turn allows the breeders to choose the plants with the desired traits effectively. The use of AI technologies can enhance the breeding process through the use of simulation of breeding results, hence cutting down on the time and resources needed for the conventional trial and error methods. Concerns on data quality, model interpretability and ethical issues need to be addressed so that the application of AI in breeding is reliable and devoid of ethical concerns. Also, the lack of advanced computing infrastructure and skilled personnel is a challenge to many breeders especially in developing countries. The prospects of artificial intelligence (AI) and machine learning (ML) in plant breeding exhibit considerable promise. The continuous advancements in computational biology, genomics, and data analytics will substantially enhance the capabilities of artificial intelligence-driven breeding systems. The integration of artificial intelligence (AI) and machine learning (ML) into plant breeding methodologies has the potential to revolutionize crop improvement efforts, therefore laying the foundation for sustainable agriculture and food security in the context of a changing climate.
Varma et al. (Sun,) studied this question.