Microbiome-based control of postharvest mycotoxin-producing fungi in cereals.

Mycotoxin contamination caused by fungal pathogens remains a persistent threat to global food security, while the long-standing reliance on synthetic fungicides is increasingly challenged by resistance development and toxicological concerns. Here, we synthesize current knowledge on mycotoxin ecology and present to our knowledge, for the first time an integrated functional framework linking postharvest microbiome modulation with practical control strategies. Our analysis shows that beneficial microbial consortia suppress mycotoxigenic fungi through complementary mechanisms, including competitive exclusion, mycoparasitism, and enzymatic detoxification. We further classify these microbial antagonists according to their ecological niches and functional interactions within stored-product systems, providing a high-resolution perspective on the role of the plant-associated microbiome as a target for intervention. In parallel, we evaluate recent advances in multi-omics approaches and artificial intelligence (AI), highlighting their potential to shift mycotoxin management from reactive detection to proactive risk prediction. The integration of sensor-based storage systems, automated monitoring, and explainable AI (XAI) is proposed as a scalable strategy for real-time identification and mitigation of contamination risks. We conclude that postharvest mycotoxin control is moving toward a digital-biological paradigm, in which microbiome engineering and intelligent monitoring systems provide sustainable, residue-free alternatives to chemical interventions. Future research should focus on validating these integrated approaches under real storage conditions, improving their scalability, and ensuring accessibility across diverse agricultural systems.