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With promising results and enormous capability, deep learning technology has attracted more and more attention to both theoretical research and applications for a variety of image processing and computer vision tasks. In this paper, we investigate 32 research contributions that apply deep learning techniques to the agriculture domain. Different types of deep neural network architectures in agriculture are surveyed and the current state-of-the-art methods are summarized. This paper ends with adoi:10.3745/jips.04.0187 dblp:journals/jips/RenKJ20 fatcat:6qrdqx5m3rgivhs3qoi4qjzcnu