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Position: Home > Articles > Deep learning for smart agriculture: Concepts, tools, applications, and opportunities International Journal of Agricultural and Biological Engineering 2018,11 (4)

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

作者：

Nanyang Zhu;Xu Liu;Ziqian Liu;Kai Hu;Wang Yingkuan;Jinglu Tan;Min Huang;Qibing Zhu;Xunsheng Ji;Yongnian Jiang;Ya Gu

单位：

;NULL;1. Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122, China;4 Department of Bioengineering, University of Missouri, Columbia, MO 65211, USA;#R##N#2. School of Internet of Things, Jiangnan University, Wuxi 214122, China;3. Chinese Academy of Agricultural Engineering, Beijing 100125, China;5. Jiangsu Zhongnong IoT Technology Co., LTD, Yixing 214200, Chin

关键词：

dl;agriculture;applications;major;smart agriculture;researcher

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

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