Design and Implementation of an Integrated Sensor Network for Monitoring Abiotic Parameters During Composting

Efficient composting is essential for sustainable organic waste management, yet conventional monitoring approaches are limited by single-parameter measurements and delayed response. This study presents an integrated sensor–AI framework designed to capture the interaction between thermal, chemical, and environmental factors governing composting. A distributed in-pile sensor network continuously measured temperature, moisture, and pH, while ambient parameters and gaseous emissions (O2, CO2, CH4) were recorded to validate process dynamics. Statistical analyses, including correlation and regression modeling, were applied to quantify parameter interdependencies and the influence of external conditions. Results showed strong positive associations between temperature, moisture, and CO2, and an inverse relationship with O2, indicating active microbial respiration and accelerated decomposition. The validated sensors maintained high accuracy (±0.5 °C, ±3%, ±0.1 pH units) and supported real-time feedback control, leading to improved nutrient enrichment (notably N, P, and K) in the final compost. The framework demonstrates a transition from static measurement to intelligent, feedback-driven management, providing a scalable and reliable platform for optimizing compost quality and advancing sustainable waste-to-resource applications.