Deep learning in agriculture: A survey
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Publication date
2018-02-22ISSN
0168-1699
Abstract
Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
Document Type
Article
Document version
Accepted version
Language
English
Subject (CDU)
63 - Agriculture and related sciences and techniques
Pages
54
Publisher
Elsevier
Is part of
Computers and Electronics in Agriculture
Citation
Kamilaris, Andreas, and Francesc X. Prenafeta-Boldú. 2018. "Deep Learning In Agriculture: A Survey". Computers And Electronics In Agriculture 147: 70-90. Elsevier BV. doi:10.1016/j.compag.2018.02.016.
Grant agreement number
EC/H2020/665919/EU/Opening Sphere UAB-CEI to PostDoctoral Fellows/P-SPHERE
Program
Sostenibilitat en Biosistemes
This item appears in the following Collection(s)
- ARTICLES CIENTÍFICS [3044]
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/