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Deep learning for smart agriculture: Concepts, tools, applications, and opportunities

文献类型： 外文期刊

作者： Zhu, Nanyang 1 ; Liu, Xu 1 ; Liu, Ziqian 1 ; Hu, Kai 1 ; Wang, Yingkuan 3 ; Tan, Jinglu 4 ; Huang, Min 1 ; Zhu, Qibing 1 ; J 1 ;

作者机构： 1.Jiangnan Univ, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China

2.Jiangnan Univ, Sch Internet Things, Room C510, Wuxi 214122, Peoples R China

3.Chinese Acad Agr Engn, Beijing 100125, Peoples R China

4.Univ Missouri, Dept Bioengn, Columbia, MO 65211 USA

5.Jiangsu Zhongnong IoT Technol Co LTD, Yixing 214200, Peoples R China

关键词： deep learning; smart agriculture; neural network; convolutional neural networks; recurrent neural networks; generative adversarial networks; artificial intelligence; image processing; pattern recognition

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.032； 五年影响因子：2.137 ）

ISSN： 1934-6344

年卷期： 2018 年 11 卷 4 期

页码：

收录情况： SCI

摘要： In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.

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