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Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions

Abstract : It is evident that machine learning algorithms are being widely impacting industrial applications and platforms. Beyond typical research experimentation scenarios, there is a need for companies that wish to enhance their online data and analytics solutions to incorporate ways in which they can select, experiment, benchmark, parameterise and choose the version of a machine learning algorithm that seems to be most appropriate for their specific application context. In this paper, we describe such a need for a big data platform that supports food data analytics and intelligence. More specifically, we introduce Agroknow’s big data platform and identify the need to extend it with a flexible and interactive experimentation environment where different machine learning algorithms can be tested using a variation of synthetic and real data. A typical usage scenario is described, based on our need to experiment with various machine learning algorithms to support price prediction for food products and ingredients. The initial requirements for an experimentation environment are also introduced.
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https://hal.inria.fr/hal-03361891
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Submitted on : Friday, October 1, 2021 - 3:40:56 PM
Last modification on : Wednesday, November 3, 2021 - 7:05:30 AM
Long-term archiving on: : Sunday, January 2, 2022 - 7:28:01 PM

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Ioanna Polychronou, Panagis Katsivelis, Mihalis Papakonstantinou, Giannis Stoitsis, Nikos Manouselis. Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions. 13th International Symposium on Environmental Software Systems (ISESS), Feb 2020, Wageningen, Netherlands. pp.165-172, ⟨10.1007/978-3-030-39815-6_16⟩. ⟨hal-03361891⟩

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