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

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 757 - 757

Published: March 20, 2025

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

Language: Английский

Comparative analysis of chloroplast genomes and phylogenetic relationships of different pitaya cultivars

BMC Genomics, Journal Year: 2025, Volume and Issue: 26(1)

Published: May 9, 2025

Language: Английский