  Plant diseases are considered one of the main factors influencing food
production and minimize losses in production, and it is essential that crop
diseases have fast detection and recognition. The recent expansion of deep
learning methods has found its application in plant disease detection, offering
a robust tool with highly accurate results. In this context, this work presents
a systematic review of the literature that aims to identify the state of the
art of the use of convolutional neural networks(CNN) in the process of
identification and classification of plant diseases, delimiting trends, and
indicating gaps. In this sense, we present 121 papers selected in the last ten
years with different approaches to treat aspects related to disease detection,
characteristics of the data set, the crops and pathogens investigated. From the
results of the systematic review, it is possible to understand the innovative
trends regarding the use of CNNs in the identification of plant diseases and to
identify the gaps that need the attention of the research community.
