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

• DOI: 10.25165/j.ijabe.20181104.4475
• Publication Date: 2018-09-11T21:40:06Z
• Authors: Nanyang Zhu; ; Xu Liu; Ziqian Liu; Kai Hu; Yingkuan Wang; Jinglu Tan; Min Huang; Qibing Zhu; Xunsheng Ji; Yongnian Jiang; Ya Guo

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent (RNN) and Generative Adversarial (GAN), has been widely studied applied in various fields including agriculture. Researchers agriculture often use software frameworks without sufficiently examining ideas mechanisms a technique. This article provides concise summary major DL algorithms, concepts, limitations, implementation, training processes, example codes, to help researchers gain holistic picture techniques quickly. Research on applications is summarized analyzed, future opportunities are discussed this paper, which expected better understand learn quickly, further facilitate data analysis, enhance related research agriculture, thus promote effectively. Keywords: deep learning, smart neural network, convolutional networks, recurrent generative adversarial artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Z Q, Hu K, Wang Y Tan J L, et al. learning for agriculture: Concepts, tools, applications, opportunities. Int Agric & Biol Eng, 2018; 11(4): 32-44.