Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review
It addresses the need for fast detection of plant diseases to minimize food production losses, but it is incremental as it reviews existing work rather than proposing new methods.
This systematic review analyzed 121 papers from the last ten years to identify the state of the art in using convolutional neural networks for plant disease recognition, highlighting trends and gaps in the field.
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.