Model-based digital platform for real-time ethylene prediction in fruit storage

• Applicant: Dr.-Ing. Akshay Dagadu Sonawane
• Subject Area: Biomedical Systems Technology ; Electronic Semiconductors, Components and Circuits, Integrated Systems, Sensor Technology, Theoretical Electrical Engineering ; Plant Cultivation, Plant Nutrition, Agricultural Technology
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 567650936

Maintaining the quality of fresh produce during storage requires effective ethylene control, as this natural gas accelerates ripening. Thus, for effective control, real-time monitoring of ethylene is essential. Conventional ethylene detection methods are costly, and complex, and demand frequent calibration and manual intervention. This study presents a novel IoT-driven approach to predicting ethylene levels in real-time using basic sensor data, including temperature, O2, and CO2. By integrating state observers and spatial modelling techniques, the system minimises sensor noise and enhances prediction accuracy. Additionally, Monte Carlo simulations assess model reliability, with validation conducted through controlled experiments. This approach eliminates the need for expensive and imprecise ethylene sensors, offering a cost-effective and practical solution for fruit storage facilities. Moreover, the study aims to enhance the understanding of ethylene dynamics, enabling better management of storage conditions to extend shelf life and maintain fruit quality.

• DFG Programme: Research Grants
• Co-Investigators: Dr.-Ing. Reiner Jedermann; Dr.-Ing. Pramod Mahajan