Smart vermicomposting systems integrating iot and machine learning: A review of intelligent bioconversion technologies for sustainable waste management

This review provides a comprehensive analysis of emerging smart vermicomposting systems that integrate Internet of Things (IoT) technologies with machine learning (ML) models to improve the precision, reliability, and sustainability of organic waste bioconversion. Continuous, high-resolution monitoring of crucial physicochemical parameters—such as temperature, moisture content, pH, and oxygen concentration—enables closed-loop feedback control and automated actuation, ensuring stable microenvironmental conditions that maximize earthworm activity and microbial decomposition. Machine learning algorithms, including regression, classification, and anomaly detection models, process complex sensor data streams to provide actionable insights for predicting compost maturity, detecting process deviations, and optimizing operational parameters. These intelligent approaches enhance compost uniformity, nutrient recovery efficiency, and process stability. The integration of IoT and ML technologies reduces manual supervision, increases throughput, and supports scalable deployment from household systems to industrial waste-management infrastructures. The review also discusses key technological advancements, including sensor network optimization, wireless communication architectures, cloud-based data management frameworks, and adaptive predictive modeling approaches. Persistent challenges—such as sensor calibration drift, power constraints, environmental variability, and real-time data processing limitations— are critically examined, and strategies for developing robust, generalizable ML models suitable for dynamic composting environments are presented. Overall, this review highlights the transformative potential of intelligent, data-driven vermicomposting platforms in promoting sustainable waste management, advancing resource recovery efficiency, and contributing to circular economy and climate-resilient environmental objectives.