Intelligent plant disease diagnosis using convolutional neural network: a review

Diana Susan Joseph, Pranav M. Pawar, Rahul Pramanik

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


In recent times use of different technologies for intelligent crop production is growing. To increase the production of crops, diagnosing a plant disease is very important. Plant diseases can be identified using various techniques like image processing, machine learning, deep learning, etc. Among these techniques deep learning, especially deep learning using convolutional neural networks (CNN) has proved to be more efficient in recent years compared to other methods. This manuscript focuses mainly on the diseases affecting on eleven (11) different plants and how the diseases can be identified from plant leaf images using CNN based deep learning models. This review can help the researchers to get a brief overview of how state-of-the-art CNN models can be used for disease diagnosis in plants, and an overview of the state-of-the-art studies that have used visualization techniques to identify the disease spots for better diagnosis. The review also summarises the studies that have used hyperspectral images for plant disease diagnosis and various data sources used by different studies. The challenges that currently exist while developing a plant disease diagnostic system and the shortcomings and open areas for research have also been discussed in this manuscript.

Original languageEnglish
Pages (from-to)21415-21481
Number of pages67
JournalMultimedia Tools and Applications
Issue number14
StatePublished - Jun 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Convolutional neural networks
  • Deep learning
  • Leaf images
  • Plant disease classification and identification


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