Open AccessFrom machine learning to deep learning in agriculture – the quantitative review of trends
Author(s) -
Kristian Đokić,
Lucija Blašković,
Dubravka Mandušić
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/614/1/012138
Subject(s) - artificial intelligence , deep learning , computer science , machine learning , scopus , field (mathematics) , artificial neural network , agriculture , geography , political science , mathematics , archaeology , medline , pure mathematics , law
In the last two decades, we have witnessed the intensive development of artificial intelligence in the field of agriculture. In this period, the transition from the application of simpler machine learning algorithms to the application of deep learning algorithms can be observed. This paper provides a quantitative overview of papers published in the past two decades, thematically related to machine learning, neural networks, and deep learning. Also, a review of the contribution of individual countries was given. The second part of the paper analyses trends in the first half of the current year, with an emphasis on areas of application, selected deep learning methods, input data, crop mentioned in the paper and applied frameworks. Scopus and Web of Science citation databases were used.