A Time-Temperature Dataset for the Strawberry Cold Chain Across Multiple Shipments and Locations

TL;DR

This paper presents a detailed, location-aware temperature dataset from six strawberry shipments across the US, capturing temperature profiles at multiple points and layers to aid research in cold chain management and food transportation.

Contribution

It introduces a comprehensive, multi-location temperature dataset from real-world strawberry shipments, enabling improved analysis of cold chain conditions and potential quality impacts.

Findings

Temperature data collected at 54 points across shipments

High-resolution temperature profiles during transportation

Data supports research in food engineering and machine learning

Abstract

This article describes location aware temperature profiles from six strawberry shipments across the continental United States. Three pallets were instrumented in each shipment with three vertically placed loggers to take a longitudinal and latitudinal snapshot of 9 strategically different locations (including the top, middle and bottom layers of the pallets placed in the back, middle and the front of the shipping container) for a combined 54 measurement points across shipments of varying lengths. The sensors were instrumented in the field, right at the point of harvest, recorded temperatures every every 5 to 10 minutes depending on the shipment, and uploaded their data periodically via cellular radios on each device. The data is a result of significant collaboration between stakeholders from farmers to distributors to retailers to academics, which can play an important role for researchers and educators in food engineering, cold-chain, machine learning, and data mining, as well as in other disciplines related to food and transportation.