Composting is a vital component for sustainable waste management, transforming organic residues into nutrient-rich amendment while reducing greenhouse gas emissions and supporting circular agriculture. Global organic waste volumes continue to rise, making an efficient composting process essential for maintaining soil health and mitigating the effects of climate change. The performance of the composting process depends on controlling abiotic parameters especially temperature, moisture, pH, and gaseous composition that regulate microbial activity and decomposition rates. 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.   Keywords: composting; sensor technologies; machine learning models; real-time monitoring; nutrient enhancement
Figure 1: System architecture for real-time compost monitoring and control
Figure 2: Normalized comparison of the temperature, moisture, and pH readings. Normalized comparison of temperature (a,d), moisture (b,e), and pH (c,f) between sensor-based and traditional methods across composting phases. Bar plots (a–c) show close agreement between methods, while difference plots (d–f) highlight negligible mean bias, validating sensor accuracy and reliability for real-time compost monitoring
Figure 3: Temperature, moisture, and pH trends for the sensors vs. references. Note: Different lowercase letters (a, b) above the points indicate significant differences (p < 0.05) among composting phases based on post-hoc multiple comparison tests following one-way ANOVA. Phases sharing at least one common letter (ab) are not significantly different from each other, whereas phases with different letters differ significantly
Reference:
Abdulqader Ghaleb Naser, Nazmi Mat Nawi, Mohd Rafein Zakaria, Muhamad Saufi Mohd Kassim, Azimov Abdugani Mutalovich, Kamil Kayode Katibi (2025). Design and Implementation of an Integrated Sensor Network for Monitoring Abiotic Parameters During Composting. Sustainability 17(21), 9780
Link:
https://doi.org/

Date of Input: 30/04/2026 | Updated: 30/04/2026 | ainzubaidah