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

Dae-Kyoo Kim|정동원|Chengjuan Ren
한국정보처리학회|2020.10|JIPS(Journal of Information Processing Systems) (Journal of Information Processing Systems)|제16권 제5호|pp. 1015-1033|DOI 10.3745/JIPS.04.0187

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키워드

SurveyDeep LearningAgricultureState-of-the-Art

초록

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

Abstract

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

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