Deep learning in agriculture: A survey
This document contains embargoed files until 2020-02-22
Prenafeta-Boldú, Francesc X.
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.
63 - Agricultura. Silvicultura. Zootècnia. Caça. Pesca
Is part of
Computers and Electronics in Agriculture
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
Agrosistemes i Medi Ambient
Gestió Integral de Residus Orgànics
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