Open AccessDeep learning and its role in smart agriculture
Author(s) -
V S Magomadov
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1399/4/044109
Subject(s) - deep learning , agriculture , artificial intelligence , field (mathematics) , computer science , machine learning , data science , mathematics , geography , archaeology , pure mathematics
Deep learning is a data analysis and image-processing method, which has recently gained a lot of attention as a tool, which has great potential and promising results. There are many different fields that deep learning has been applied to and it is also being applied to the field of agriculture. The purpose of this paper is to explore deep learning in terms of agriculture and food production. The performance of deep learning in agriculture is the focus of this paper comparing it to other existing artificial intelligence models, which have been used in agriculture. In addition, several types of deep learning models are covered and their differences are explained. The paper explains why some deep learning models are better equipped to be used in the field of agriculture than other models.