Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Version 1 : Received: 24 February 2022 / Approved: 28 February 2022 / Online: 28 February 2022 (03:14:56 CET)

A peer-reviewed article of this Preprint also exists.

Cravero, A.; Pardo, S.; Sepúlveda, S.; Muñoz, L. Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review. Agronomy 2022, 12, 748. Cravero, A.; Pardo, S.; Sepúlveda, S.; Muñoz, L. Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review. Agronomy 2022, 12, 748.

Journal reference: Agronomy 2022, 12, 748
DOI: 10.3390/agronomy12030748

Abstract

Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, and climate change. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in Agricultural Big Data. We conducted a Systematic Literature Review applying the PRISMA protocol. This review includes 30 papers, published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, the use of machine learning techniques, as well as the main technologies used. A major challenge is the design of Agricultural Big Data architectures, due to the need to modify the set of technologies adapting the machine learning techniques, as the volume of data increases.

Keywords

big data; machine learning; agriculture; challenges; systematic literature review

Subject

BIOLOGY, Agricultural Sciences & Agronomy

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