The fungal bioluminescence pathway (FBP) catalyzes the oxidation of endogenous caffeic acid to produce green bioluminescence through an enzymatic cascade. Genetic engineering of FBP into plants creates autoluminescent specimens that circumvent the substrate limitations of conventional reporter systems. These transgenic plants serve dual functions as aesthetic displays and versatile biosensing platforms, enabling applications in real-time gene expression monitoring, continuous environmental surveillance, and non-invasive bioimaging, offering novel opportunities for horticultural production, environmental conservation, and bioengineering applications. This review synthesizes current advances in plant FBP engineering and explores how machine learning approaches can optimize autoluminescent phenotypes, thereby accelerating innovation in agricultural biotechnology, environmental sensing, and synthetic biology applications.
Keywords: artificial intelligence; caffeic acid; fungal bioluminescence pathway; genetic engineering; plant autoluminescence; synthetic biology; transgene.