Articles
Sensors and modelling applied to fresh produce packaging and storage
Article number
1456_34
Pages
261 – 268
Language
English
Abstract
The optimization of fresh produce packaging and storage is key to reducing postharvest losses, maintaining quality, and improving energy efficiency.
This paper provides an integrated outlook on recent advances developed by the author’s research group, combining sensor technologies, predictive modelling, and control strategies to enhance postharvest management.
A suite of sensors that measures gas, airflow, condensation, and heat flux were developed, especially for apple storage to collect real-time data on microclimate conditions and physiological responses.
These sensors facilitated the monitoring of critical variables such as respiration rate, condensation events, and heat exchange under fluctuating temperature scenarios.
Building on this data, mathematical models were developed to simulate gas exchange, transpiration, condensation risk, and ethylene accumulation in packaged fruit, supporting the design of equilibrium modified atmosphere packaging.
Further developments include an ethylene diffusion model for optimal scavenger placement and an IoT-based predictive system for condensation and mass loss.
A model-driven microcontroller system was also implemented to dynamically regulate O2 and CO2 levels in storage using simple hardware components.
Together, these innovations establish a foundation for intelligent, data-driven storage systems capable of responding to environmental fluctuations and physiological processes in real-time.
The integrated approach offers a pathway toward scalable, sensor-assisted postharvest solutions that improve shelf life and reduce waste across the supply chain.
This paper provides an integrated outlook on recent advances developed by the author’s research group, combining sensor technologies, predictive modelling, and control strategies to enhance postharvest management.
A suite of sensors that measures gas, airflow, condensation, and heat flux were developed, especially for apple storage to collect real-time data on microclimate conditions and physiological responses.
These sensors facilitated the monitoring of critical variables such as respiration rate, condensation events, and heat exchange under fluctuating temperature scenarios.
Building on this data, mathematical models were developed to simulate gas exchange, transpiration, condensation risk, and ethylene accumulation in packaged fruit, supporting the design of equilibrium modified atmosphere packaging.
Further developments include an ethylene diffusion model for optimal scavenger placement and an IoT-based predictive system for condensation and mass loss.
A model-driven microcontroller system was also implemented to dynamically regulate O2 and CO2 levels in storage using simple hardware components.
Together, these innovations establish a foundation for intelligent, data-driven storage systems capable of responding to environmental fluctuations and physiological processes in real-time.
The integrated approach offers a pathway toward scalable, sensor-assisted postharvest solutions that improve shelf life and reduce waste across the supply chain.
Authors
P.V. Mahajan, A.D. Sonawane, T.G. Hoffmann, Y.B. Kalnar, R. Jedermann
Keywords
MAP, CA storage, gas exchange, mass loss, condensation, cold chain, digital twin
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