Deep learning for smart agriculture: Concepts, tools, applications, and opportunities
Deep learning for smart agriculture: Concepts, tools, applications, and opportunities
复制标题
智慧农业的深度学习：概念、工具、应用和机遇
DOI：
10.25165/j.ijabe.20181104.4475
复制
发表时间：
2018-07-01
影响因子：
2.4
通讯作者：
Guo, Ya
中科院分区：
文献类型：
--
作者：
Zhu, Nanyang;Liu, Xu;Guo, Ya
近年来，深度学习（DL），如卷积神经网络（CNN），递归神经网络（RNN）和生成对抗网络（GAN）的算法，已被广泛研究并应用于包括农业在内的各个领域。农业领域的研究人员经常使用软件框架，而没有充分研究技术的思想和机制。本文简要总结了主要的深度学习算法，包括概念、局限性、实现、训练过程和示例代码，以帮助农业研究人员快速全面了解主要的深度学习技术。本文对农业领域的深度学习应用研究进行了总结和分析，并对未来的发展趋势进行了展望，以期帮助农业领域的研究人员更好地理解深度学习算法，快速掌握深度学习的主要技术，进一步促进数据分析，加强农业领域的相关研究，从而有效地推动深度学习应用。
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.