Mark Swainson

TL;DR: While DLT has the potential to transform food systems, this can only be fully realized through the global development and agreement on suitable data standards and governance, and key technical issues need to be resolved including challenges with DLT scalability, privacy and data architectures.

TL;DR: In this article, the authors discussed the adoption of RAS in the food industry from a supply chain perspective with regard to the supply chain operations. But, most of the current literature focuses on the technological impact of the RAS.

TL;DR: The proposed framework is the first to employ deep neural networks for end-to-end automatic use by date recognition in retail packaging photos, capable of achieving very good levels of performance on all the aforementioned tasks, despite the varied textual/pictorial content complexity found in food packaging design.

TL;DR: In this article, a deep Bayesian self-training methodology for automatic data annotation, by leveraging predictive uncertainty estimates using variational inference and modern neural network (NN) architectures, as well as a practical adaptation procedure for handling high label variability between different dataset distributions through clustering of NN latent variable representations is presented.

TL;DR: Adding 15–30% TSP with or without yeast inclusion could be beneficial for the development of future meat hybrids with acceptable sensory quality.