Authors :
Swati Dilip Kale; Shailaja C.Patil
Volume/Issue :
Volume 7 - 2022, Issue 3 - March
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/365ImHj
DOI :
https://doi.org/10.5281/zenodo.6452156
Abstract :
Perishable food preservation is an important
aspect of cold chain logistics operations. Changes in
humidity and temperature in cold reefers typically cause
perishable food to deteriorate. The current cold chain
design in India is ineffective at maintaining these values
constant during transport. Therefore, a smart cold chain
system is required to avoid food wastage in the cold chain
network. In this study, an IoT-based system is developed
to monitor the temperature and humidity of perishables
during transit considering various vehicle characteristics.
The study suggests measuring the Mean Kinetic
Temperature (MKT) that takes into account the
biochemical changes in food caused by temperature
fluctuations. Machine learning algorithms are being used
to estimate the quality of perishables, which is a significant
advancement in cold chain technology. Machine learning
algorithms improve the accuracy of time-temperature data
prediction, thereby preserving food quality during
transportation. The cloud and a mobile app are used to
send an early warning message about temperature abuse
to the concerned person. In addition, a comparative
analysis of algorithms is carried out to recommend the best
algorithm for prediction. The outcomes are compared to
those of real-time applications.
Keywords :
Cold Chain; IoT; Perishable Food Quality; MKT; Machine Learning Algorithms
Perishable food preservation is an important
aspect of cold chain logistics operations. Changes in
humidity and temperature in cold reefers typically cause
perishable food to deteriorate. The current cold chain
design in India is ineffective at maintaining these values
constant during transport. Therefore, a smart cold chain
system is required to avoid food wastage in the cold chain
network. In this study, an IoT-based system is developed
to monitor the temperature and humidity of perishables
during transit considering various vehicle characteristics.
The study suggests measuring the Mean Kinetic
Temperature (MKT) that takes into account the
biochemical changes in food caused by temperature
fluctuations. Machine learning algorithms are being used
to estimate the quality of perishables, which is a significant
advancement in cold chain technology. Machine learning
algorithms improve the accuracy of time-temperature data
prediction, thereby preserving food quality during
transportation. The cloud and a mobile app are used to
send an early warning message about temperature abuse
to the concerned person. In addition, a comparative
analysis of algorithms is carried out to recommend the best
algorithm for prediction. The outcomes are compared to
those of real-time applications.
Keywords :
Cold Chain; IoT; Perishable Food Quality; MKT; Machine Learning Algorithms
