Maize leaf disease classification using convolutional neural network

2021 ◽  
Author(s):  
S. Malliga ◽  
P. S. Nandhini ◽  
S. V. Kogilavani ◽  
R. Jaya Harini ◽  
S. Jaya Shree ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Disease Classification ◽  
Maize Leaf ◽  
Leaf Disease

A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

2018 ◽  
Vol 61 (5) ◽  
pp. 1461-1474 ◽  
Author(s):  
Zhongqi Lin ◽  
Shaomin Mu ◽  
Aiju Shi ◽  
Chao Pang ◽  
Xiaoxiao Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Graphics Processing Units ◽  
High Efficiency ◽  
Saliency Detection ◽  
Machine Learning Algorithms ◽  
Brown Spot ◽  
Maize Leaf ◽  
Leaf Disease ◽  
Fully Connected

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

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

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 707
Author(s):  
Jinzhu Lu ◽  
Lijuan Tan ◽  
Huanyu Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Classification ◽  
Classification Methods ◽  
Widespread Application ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Traditional Classification

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.

Sign in / Sign up

Export Citation Format

Share Document