Agricultural and Food SciencesComputer Science

Nicoleta Tantalaki, S. Souravlas, M. Roumeliotis

2019.7.22Journal of Agricultural & Food Information

DOI: 10.1080/10496505.2019.1638264

tlooto Summary

The research dedicated to applications of data science techniques, and especially machine learning techniques, in relevant agricultural systems are reviewed, which reveal opportunities and promising areas of use.

Abstract

Abstract In this paper, we provide a review of the research dedicated to applications of data science techniques, and especially machine learning techniques, in relevant agricultural systems. Big data technologies create new opportunities for data intensive decision-making. We review works in agriculture that employ the practice of big data analysis to solve various problems, which reveal opportunities and promising areas of use. The high volume and complexity of the data produced pose challenges in successfully implementing precision agriculture. Machine learning seems promising to cope with agricultural big data, but needs to reinvent itself to meet existing challenges.

Citation format

TANTALAKI, Nicoleta; SOURAVLAS, S.; ROUMELIOTIS, M. Data-driven decision making in precision agriculture: The rise of big data in agricultural systems. Journal of Agricultural & Food Information, 2019, 20: 344–380.