A Time-Temperature Dataset for the Strawberry Cold Chain Across Multiple Shipments and Locations
arXiv:2103.12895 · doi:10.1016/j.jfoodeng.2021.110477
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.
References in corpus (8)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
- Dilated Recurrent Neural Networks
- Deep Divergence-Based Approach to Clustering
- Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data
- A Time-Temperature Dataset for the Strawberry Cold Chain Across Multiple Shipments and Locations
- Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization