Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review

TL;DR

This systematic review analyzes 121 recent studies on using convolutional neural networks for plant disease detection, highlighting current trends, challenges, and research gaps in the field.

Contribution

It provides a comprehensive overview of CNN-based plant disease recognition research, identifying innovative trends and gaps over the past decade.

Findings

CNN methods have significantly improved plant disease detection accuracy.

Most studies focus on specific crops and pathogens, revealing research gaps.

Emerging trends include data augmentation and transfer learning techniques.

Abstract

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.