Automated compost monitoring with low-cost RFID, drones, and machine learning for improved control and pathogen safety

Composting turns organic waste into nutrient-rich material, but maintaining the right temperature and moisture is crucial to kill harmful pathogens and meet safety regulations. Traditional monitoring methods, like manual sampling or expensive sensors (e.g., Reotemp, HOBO loggers), are labor-intensive, costly, and fail to capture temperature and moisture variations in large compost piles (windrows). These variations can lead to cold spots, where pathogens survive, or hotspots, which pose fire risks. This project will develop an intelligent compost monitoring system using low-cost, battery-free RFID sensors ($3–4 per unit), which are 80–90% cheaper than current solutions.

These sensors will be placed throughout compost piles to measure temperature and moisture. Drones equipped with RFID readers and LiDAR will autonomously collect data, recording precise locations. Machine learning algorithms will analyze the data, identifying cold and hot spots in real time. The information will be displayed on a digital dashboard, helping compost operators adjust turning schedules, ensure uniform heating, and reduce manual labor. The system will also automate compliance reporting to meet FDA §112.60(b)(2) standards. By improving monitoring accuracy, reducing costs, and supporting data-driven decisions, this system will make composting safer, more efficient, and environmentally sustainable.

Composting transforms organic waste into nutrient-rich soil amendments but requires strict temperature and moisture control to ensure pathogen inactivation and regulatory compliance. Current monitoring methods—manual sampling or expensive wireless sensors (e.g., Reotemp, HOBO loggers) which limit data resolution and increase labor costs. Large windrows, often spanning hundreds of feet, exhibit spatiotemporal temperature and moisture variations, where cold spots allow pathogen survival and hotspots pose combustion risks. Conventional monitoring methods fail to capture these variations comprehensively, jeopardizing compost quality and regulatory compliance. This project will develop and validate an intelligent compost monitoring system that integrates low-cost, battery-free RFID sensors ($3–4/unit, 80–90% cheaper than existing solutions), drone-based readers, and machine learning analytics to enable real-time, high-density temperature and moisture monitoring. Drones equipped with RFID readers and LiDAR will autonomously collect geo-located sensor data, which will be processed through a digital dashboard for 3D compost pile visualization, risk hotspot detection, and decision support. This system also automates compliance reporting to meet FDA §112.60(b)(2) standards. 

The project has three key objectives: (1) Deploying a dense RFID sensor network to capture compost process indicators, (2) Developing a visualization and analytics platform for real-time monitoring and risk detection, and (3) Validating the system over five composting cycles at two Tennessee facilities. 

The systems will be assessed for its ability to accurately capture spatiotemporal temperature variations, reduce labor costs, and improve regulatory compliance. By enabling compost operators to detect cold spots, ensure uniform heating, and optimize turning schedules in real time, this solution will enhance process efficiency and streamline compliance documentation. Ultimately, this innovation supports safer, more sustainable composting operations by providing automated, data-driven decision support.

Objective 1: Deploy and refine an adaptable RFID-based sensor network for reliable data recording of key metrics (temperature and moisture) across compost piles. 

Objective 2: Develop a digital toolbox for data visualization, process classification, and risk hotspot mapping. 

Objective 3: Conduct experimental validation of the sensor network and digital toolbox in commercial composting facilities to assess accuracy, efficiency, and feasibility in real-world conditions.

This project is ongoing. A final report will be provided when the project is finished.