Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

2021/07/27 by Jinzhu Lu, Lijuan Tan, Huanyu Jiang · 3 citations
Agricultural and Biological Sciences · Chemistry · #Leaf Properties and Growth Measurement #Smart Agriculture and AI #Spectroscopy and Chemometric Analyses

paper · pdf · doi:10.3390/agriculture11080707

openalex publication_date 2021/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

Citations

• ImageNet classification with deep convolutional neural networks
• An open access repository of images on plant health to enable the development of mobile disease diagnostics
• On the Origin of Deep Learning
• Identification of plant leaf diseases using a nine-layer deep convolutional neural network
• Recent advances in convolutional neural networks
• Using deep transfer learning for image-based plant disease identification
• Identification of rice plant diseases using lightweight attention networks
• Tomato plant disease detection using transfer learning with C-GAN synthetic images
• Generative Adversarial Networks

Cited by

• Remote sensing-based detection of brown spot needle blight: a comprehensive review, and future directions
• SugarcaneShuffleNet: A Very Fast, Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases
• Transfer Learning-Based CNN Models for Plant Species Identification Using Leaf Venation Patterns

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• A comprehensive review on Plant Leaf Disease detection using Deep learning
• Plant Disease Detection and Classification by Deep Learning—A Review
• Leaf diseases detection using deep learning methods
• Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches