From signals to solutions: stress-induced leaf senescence and synthetic biology and AI approaches for crop resilience

Abstract The quality and quantity of plant traits are critically linked to the coordinated onset of leaf senescence. However, both external environmental factors and internal hormones may accelerate leaf senescence process, resulting in various physiological changes, including chlorophyll degradation, anthocyanin biosynthesis, nutrient recycling, and the activation of senescence-associated genes (SAGs). A comprehensive understanding of the signaling pathways involved in stress-induced leaf senescence is essential for plant breeding aimed at enhancing resistance and productivity. This review provides an extensive overview of the signaling mechanisms associated with leaf senescence triggered by abiotic and biotic stresses, including abscisic acid (ABA), darkness, nitrogen deficiency, carbon deficiency, and pathogen attack. Additionally, we discuss strategies to improve stress tolerance, yield, and quality through innovative synthetic biology approaches. Furthermore, we explore the potential applications of machine learning (ML) and deep learning (DL) in the context of senescence- and stress-related plant breeding.