Title:
Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review
Authors:
Ania Cravero1, Sebastian Pardo1, Samuel Sepúlveda1 & Lilia Muñoz2
1 Department of Computer Science and Informatics, Center for Software Engineering Studies, Universidad de La Frontera, Temuco 4780000, Chile
2 Faculty of Computer Systems Engineering, Universidad Tecnológica de Panamá, Panama City 32401, Panama
Published:
Agronomy, 21 March 2022
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.