LAUSR

During transportation, prediction of the Remaining Shelf-Life (RSL) of Fresh Fruits and Vegetables (FFVs) is critical for planning and quality cost estimation. The Internet of Things (IoT) enables measured environmental variables to be processed in real-time. However, there is a need for a validated, real-time computational method that translates environmental measurements to dynamic RSL estimates. Most existing generic RSL models for FFVs are qualitative, invasive, or static. This study establishes a generic RSL model for FFVs under dynamic and unplanned logistic conditions. The model is based on estimating the current rate of general decay based on the expected respiration rate of the product, and integrating the decay rate with respect to time. Its implementation is non-destructive, non-invasive, and does not require accelerated shelf-life experiments before deployment. In addition, since the original model is rather computationally intensive, a surrogate model was proposed to allow the model to be implemented in fast, real-time applications for ‘Edge IoT.’ Experimental validation of the model using three fresh products (strawberries, apricots, and spinach) in a domestic refrigerator resulted in a maximum deviation of 1.3 days in prediction error using the original model and 2.95 days using the surrogate model. Nonetheless, the predictions made using either the original or surrogate models were statistically sound and not significantly different from the observed shelf lives of the samples, even at the 0.01 significance level.