Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review

Maria Gerakari, Anastasios Katsileros, Konstantina Kleftogianni, Eleni Tani, Penelope J. Bebeli, Vasileios Papasotiropoulos

This review discusses the potential of artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), in advancing the genetic improvement of Solanaceous crops. AI has emerged as a powerful solution to overcome the limitations of traditional breeding techniques, which often involve time-consuming, resource-intensive processes with limited predictive accuracy. Through advanced algorithms and predictive models, ML and DL facilitate the identification and optimization of key traits, including higher yield, improved quality, pest resistance, and tolerance to extreme climatic conditions. By integrating big data analytics and omics, these methods enhance genomic selection (GS), support gene-editing technologies like CRISPR-Cas9, and accelerate crop breeding, thus enabling the development of resilient and adaptable crops. This review highlights the role of ML and DL in improving Solanaceae crops, such as tomato, potato, eggplant, and pepper, with the aim of developing novel varieties with superior agronomic and quality traits. Additionally, this study examines the advantages and limitations of AI-driven breeding compared to traditional methods in Solanaceae, emphasizing its contribution to agricultural resilience, food security, and environmental sustainability.