Integrating Genomic Selection and Machine Learning: A Data-Driven Approach to Enhance Corn Yield Resilience Under Climate Change

Abstract

Genomic selection is a revolutionary approach in breeding, exploiting genetic markers to forecast breeding values and hence accelerating the pace of traits associated with resilience, like drought tolerance, heat resistance, and pest resistance. This study addresses these challenges through ML algorithms such as random forests, support vector machines, and neural networks thereby enhancing prediction accuracy while handling complicated genomic as well as environmental datasets. Relevant ML algorithms for genomic selection are considered in this discussion, as well as strategies for data processing, feature selection, and environmental factors, including climate conditions and soil parameters. These are brought together to form predictive models that indeed cater to genotype-by-environment interactions vital for crop performance evaluation over different environmental conditions. A proposed framework integrates genomic selection with machine learning, benefiting both disciplines by developing a data-driven methodology for yield prediction in corn. The critical machine learning models to be used include multi-layer perceptron and ensemble models. A case study shows the practical applicability of the GS-ML framework, describing the dataset prepared, model testing and validation procedures, and yield resilience prediction results. The conclusion of the study states that GS and ML combined hold great promise in supporting sustainable agriculture and climate resilience. It requires further research, infrastructure development, and policy support to scale this approach across different crops and diverse climate scenarios. The combined use of genomic and ML approaches is profoundly innovative in predictive breeding and will help develop resilient agricultural systems critical for global food security under a changing climate.