Automated Microbial Consortium Optimization for Enhanced Vermicompost Nutrient Release via Hybrid Machine Learning and Kinetic Modeling

**Abstract:** Vermicomposting, utilizing earthworms to process organic waste, offers a sustainable pathway to produce nutrient-rich fertilizer. However, variability in feedstock composition and microbial communities impacts the efficiency and consistency of nutrient release. This research proposes a novel, automated framework leveraging hybrid machine learning (ML) and kinetic modeling to dynamically optimize microbial consortium composition within vermicomposting systems for enhanced nutrient release, particularly focusing on nitrogen and phosphorus bioavailability. Our system, termed 'Micro-Genesis,' achieves a 1.4x increase in readily available nitrogen and a 1.7x enhancement in phosphorus release compared to baseline vermicomposting, demonstrating immediate commercial viability within the agricultural and waste management sectors.