A Survey of Deep Learning in Agriculture: Techniques and Their Applications


Chengjuan Ren, Dae-Kyoo Kim, Dongwon Jeong, Journal of Information Processing Systems Vol. 16, No. 5, pp. 1015-1033, Oct. 2020  

10.3745/JIPS.04.0187
Keywords: Deep Learning, Agriculture, State-of-the-Art, Survey
Fulltext:

Abstract

With promising results and enormous capability, deep learning technology has attracted more and more attention to both theoretical research and applications for a variety of image processing and computer vision tasks. In this paper, we investigate 32 research contributions that apply deep learning techniques to the agriculture domain. Different types of deep neural network architectures in agriculture are surveyed and the current state-of-the-art methods are summarized. This paper ends with a discussion of the advantages and disadvantages of deep learning and future research topics. The survey shows that deep learning-based research has superior performance in terms of accuracy, which is beyond the standard machine learning techniques nowadays.


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Cite this article
[APA Style]
Chengjuan Ren, Dae-Kyoo Kim, & Dongwon Jeong (2020). A Survey of Deep Learning in Agriculture: Techniques and Their Applications. Journal of Information Processing Systems, 16(5), 1015-1033. DOI: 10.3745/JIPS.04.0187.

[IEEE Style]
C. Ren, D. Kim and D. Jeong, "A Survey of Deep Learning in Agriculture: Techniques and Their Applications," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1015-1033, 2020. DOI: 10.3745/JIPS.04.0187.

[ACM Style]
Chengjuan Ren, Dae-Kyoo Kim, and Dongwon Jeong. 2020. A Survey of Deep Learning in Agriculture: Techniques and Their Applications. Journal of Information Processing Systems, 16, 5, (2020), 1015-1033. DOI: 10.3745/JIPS.04.0187.