This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively

Setiawan, Wahyudi

Syarief, Mohammad

English

TELKOMNIKA (Telecommunication Computing Electronics and Control)

TELKOMNIKA Telecommunication, Computing, Electronics and Control 
Vol. 18, No. 3, June 2020, pp. 1376~1381 
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018 
DOI: 10.12928/TELKOMNIKA.v18i3.14840  1376 
  
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA 
Convolutional neural network  
for maize leaf disease image classification 
 
 
Mohammad Syarief, Wahyudi Setiawan 
Informatics Department, University of Trunojoyo Madura, Indonesia 
 
 
Article Info  ABSTRACT  
Article history: 
Received Aug 17, 2019 
Revised Jan 4, 2020 
Accepted Feb 26, 2020 
 This article discusses the maize leaf disease image classification.  
The experimental images consist of 200 images with 4 classes: healthy, 
cercospora, common rust and northern leaf blight. There are 2 steps: feature 
extraction and classification. Feature extraction obtains features automatically 
using convolutional neural network (CNN). Seven CNN models were  
tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, 
Inception-V3, residual network 50 (ResNet50) and ResNet101. While  
the classification using machine learning methods include k-Nearest 
neighbor, decision tree and support vector machine. Based on the testing 
results, the best classification was AlexNet and support vector machine with 
accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively. 
Keywords: 
AlexNet 
Classification 
Convolutional neural network 
k-nearest neighbor 
Maize leaf image This is an open access article under the CC BY-SA license. 
 
Corresponding Author: 
Wahyudi Setiawan,  
Informatics Department,  
University of Trunojoyo Madura, 
Raya Telang St., Perumahan Telang Inda, Telang, Kamal, Bangkalan, Jawa Timur 69162, Indonesia. 
Email: wsetiawan@trunojoyo.ac.id 
 
 
1. INTRODUCTION  
Convolutional neural network (CNN) is a development of the artificial neural network that consists 
of tens to hundreds of layers [1]. CNN is a method in deep learning that can perform various tasks such as 
image classification [2, 3], segmentation [4, 5], recognition [6, 7], and objects detection [8, 9]. CNN 
technology has grown widely including fields of medical image [10, 11], autonomous drivers [12, 13], 
robotics [14, 15], and agricultural image [16]. Many image studies have been carried out, such as disease 
classification in 15 food crops using 5 convolutional layers [17], classification of diseases in 9 class plant 
images using googleNet [18]. Mohanty et al. classified 14 types of food crops, including maize. There was  
26 class of diseases. The testing used images in vast numbers, i.e 54,306 images. Deep learning conventional 
neural network with two architecture (AlexNet and GoogleNet) was used for classification.  
The classification results showed an accuracy of 31.4% [19]. 
In this study, classification was carried out to detect diseases in maize leave images using CNN.  
One of the previous studies that carried out diseases classification of maize leaves using CNN was Sibiya & 
Sumbwanyambe [20]. They using 3 classes of disease classification: northern leaf blight, common rust,  
and cercospora. CNN architecture used was not explained in detail, but it only mentioned using 50 hidden 
layers consisting of convolution layers with filter kernels that have a median of 24, rectified linear units 
(ReLU) and pooling layers. One hundred images per class was used with a ratio of 70% for training and 30% 
for testing. The testing results showed an accuracy of 92.85% [20] 
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Convolutional neural network for maize leaf disease image classification (Mohammad Syarief) 
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Zhang classified 8 diseases in maize leaf images: southern leaf blight, brown spot, curvularia leaf 
spot, rust, dwarf mosaic, gray leaf spot, round spot, and northern leaf blight [18]. CNN architecture used was 
googleNet or Inception-V1. The experiments were conducted using 3,672 images, 80% for training and 20% 
for testing. Classification results showed an accuracy of 98.9% [18]. Hidayat classified three diseases in 
maize leaf images: common rust, cercospora, and northern leaf blight [21]. The experiments used 300 maize 
leaf images. Average accuracy result was 93.67% [21]. 
Both types of research, Sibiya & Sumbwanyambe [20] and Hidayat et al. [21], only explained  
the type of CNN layers, but the number of each layer type and detailed parameters were not explained, while 
Zhang’s [18] used existing CNN architecture, i.e GoogleNet that consists of 177 layers. There was a novelty 
in this study. First, use of 7 CNN architectures: AlexNet [22], VGG16, VGG19 [23], GoogleLet [23], 
Inception-V3 [24], ResNet50 and ResNet101 [25] and machine learning classification method (kNN, 
decision tree, SVM) to classify maize leaf diseases. Second, the percentage of accuracy increased while 
compared to the previous study.  
 
 
2. RESEARCH METHOD 
The steps for classification process using CNN are shown in Figure 1. Maize leaf images as data are 
divided into 2 parts: training and testing data. Furthermore, CNN is applied, the function of CNN as a feature 
extraction process without determining type of feature extraction as in conventional machine learning.  
The next process is classification using k-Nearest Neighbor, support machine and decision tree. 
 
 
 
 
Figure 1. The research method of maize leaf disease image classification  
 
 
2.1.  Maize leaf image 
Image data used maize leaves that size of 256x256 pixels. Data consists of 200 images which are 
divided into 4 classes, 50 images per class. Experiment data obtained from Mohanty plant village [19]. 
Examples of image data on maize leaves are shown in Figure 2. When training and testing using CNN, image 
size is adjusted to default size of each CNN architecture. Table 1 show the default input size of CNN model. 
 
 
 
 
 Figure 2. (a) Normal, (b) Cercospora, (c) Northern leaf blight, (d) Common rust 
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Table 1. Default input size of CNN 
CNN Default Input Size 
AlexNet 227×227 
VGG16 224×224 
VGG19 224×224 
GoogleNet 224×224 
Inception-V3 299×299 
ResNet50 224×224 
ResNet101 224×224 
 
 
2.2.  Convolutional neural network 
CNN consists of 2 main parts: feature extraction and classification. The feature extraction section 
includes input layer, convolutional layer with stride and padding, rectified linear unit (ReLU), pooling,  
and batch normalization layer. While the classification part consists of fully connected layer, softmax dan 
output layer. CNN architecture can have more than one type of layer [26]. CNN architectures analyzed in this 
paper were AlexNet, VGG16, VGG19, GoogleNet, Inception-V3, ResNet50, and ResNet101. Those 
architectures have 25, 41, 177 and 144 layers, respectively. Figure 3 shows a simple CNN model that has  
13 layers: 1 input layer, 3 convolutional layers with stride and padding, 3 ReLU layers, pooling layer, 2 normalization 
layer, FCL, softmax, and output layer.  
 
 
 
 
Figure 3. Simple CNN model 
 
 
AlexNet architecture has twenty-five layers [22]: Input layer, 5 convolutional layers, first 
convolutional layer has a 11×11 filter, second layer has a 5×5 filter, and third, to fifth layer have 3×3 filters. 
Furthermore, 7 ReLU layers, 2 normalization layers, 3 max-pooling layers, 3 fully connected layer,  
2 dropouts 0.5, Softmax and output layer. Visual Geometry Group (VGG) from Oxford University creates a 
VGG16 network architecture with 41 layers. VGG simplifies the processes by creating a 3×3 filter for each 
layer. Equivalent and smaller filter size used in VGG can produce more complex features and lower 
computing than AlexNet’s. VGG16 architecture consists of [23]: the input layer size is 224×224 pixels.  
13 convolutional layers. First and second convolutional layers have filter size of 64 pixels, third and fourth 
have filter size of 128 pixels, fifth to seventh have filter size of 256 pixels and eight to thirteenth have filter 
size of 512. Fifteen ReLU. 5 max-pooling, 3 fully connected layers. Two dropout 0.5, Softmax and  
output layer 
While VGG19 architecture consists of [23]: input layer is 224×224 pixels. Sixteen convolutional 
layers. First and second convolutional layers have filter size of 64 pixels, third and fourth have filter size of 
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Convolutional neural network for maize leaf disease image classification (Mohammad Syarief) 
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128 pixels, 5 to 8 have filter size of 256 pixels and the 9 to 16 have filter size of 512 pixels, 18 ReLU,  
5 max-pooling, 3 fully connected layers, 2 dropouts of 0.5 Softmax and output layer. ResNet50 dan 
ResNet101 increasing number of layers is directly proportional to the increase in learning, but it can lead to 
learning more and more difficult and accuracy decreases. Residual learning provides solutions to these 
problems. Residual Network (ResNet) is a CNN network architecture for residual learning. Residual learning 
skips layer connection. ResNet50 architecture has 177 layers, while ResNet101 has 347 layers [26].  
GoogLeNet (Inception-V1) is a CNN architecture that has 144 layers. GoogLeNet corrects 
deficiencies in VGG that require high computing, both memory and time. The working principle of Inception 
is that the network will automatically choose the best convolution results using a certain size. Filter size used 
in this architecture is 1×1 pixels, 3×3 pixels, 5×5 pixels and max-pooling 3×3 pixels. Another variant used in 
this study was Inception-V3. Inception-V3 architecture consists of 316 layers [27-29] 
 
2.3.  Classification methods 
In this study, we used three classification methods for testing: support vector machine [30],  
k-Nearest Neighbor [31] and decision tree [32]. For each testing, we configure the network layer,  
extract the features, and make classification using each method above. 
 
 
3. RESULT AND DISCUSSION 
The experiment divided into 3 scenarios, output of 7 CNN models classified with SVM, kNN and 
Decision Tree. Testing results using the CNN architecture were found in Table 2 to Table 4. Table 2 
represented classification testing using the SVM method, while Table 3 and Table 4 for classification using 
k-Nearest Neighbor and decision tree methods, respectively. 
 
 
Table 2. Testing results using SVM  
CNN model Sensitivity 
(%) 
Specificity 
(%) 
Accuracy 
(%) 
AlexNet 95.83 100 95 
Vgg16 88.4 92.03 88.3 
Vgg19 88.4 92.03 88.3 
ResNet50 87.28 90.63 86.7 
ResNet101 90 92.85 90 
GoogleNet 83.75 89.13 83.3 
Inception-V3 87.55 91.32 86.7 
Average 88.74 92.57 88.33 
 
Table 3. Testing results using kNN  
CNN model Sensitivity 
(%) 
Specificity 
(%) 
Accuracy 
(%) 
AlexNet 94.72 94.715 93.3 
Vgg16 82.23 89.65 76.7 
Vgg19 88.4 91.94 88.3 
ResNet50 73.68 87.2 75 
ResNet101 81.98 88.89 80 
GoogleNet 84.55 89.41 83.3 
Inception-V3 80.98 88.33 80 
Average 83.79 90.02 82.37 
 
 
 
Table 4. Testing results using decision tree  
CNN model Sensitivity (%) Specificity (%) Accuracy (%) 
AlexNet 74.33 84.19 73.3 
Vgg16 77.73 84.58 75 
Vgg19 76.93 86.89 76.7 
ResNet50 83.58 88.22 83.3 
ResNet101 76 83.7 73.3 
GoogleNet 73.85 85.61 75 
Inception-V3 66.53 79.93 65 
Average 75.56 84.73 74.51 
 
 
Based on the testing results above, the best classification was produced by AlexNet architecture with 
Support Vector Machine classification. It showed the best performance measures based on sensitivity, 
specificity, and accuracy of 95.83%, 100%, and 95%, respectively. Best average accuracy of 88.33%  
using SVM. Furthermore, to do validation used 10-fold cross-validation. This method will divide data into  
10 equal parts. The complete stages are as follows: 
a) First, 9 data sections are used for training, one final data section is used for testing. 
b) Second, the data section until the data is used for training, the first data section is used for testing,  
and so on.  
c) And so on until the data part 1 to 8 and 10 are used as training data, while data section 9 is used as 
testing data. 
d) Find the average value of all rounds. For more details, an illustration of 10-fold cross-validation is 
shown in Figure 4. 
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The results of 10-fold cross-validation are shown in Table 5. It using AlexNet and SVM as classification. In 
the final section, the results of 10 k cross-validation were compared with previous studies. Table 6 represents 
a comparison between this study and previous studies using maize leaf images for 
disease classification. 
 
 
 
 
Figure 4. 10-fold cross-validation 
 
 
Table 5. Performance measure of 10-fold cross-validation 
Round Sensitivity (%) Specificity (%) Accuracy (%) 
1 95.83 95.83 95 
2 90 92.86 90 
3 100 100 100 
4 87.5 88.69 85 
5 100 100 100 
6 95.83 95.83 95 
7 100 100 100 
8 80 88.89 80 
9 90 92.86 90 
10 95.83 95.83 95 
Average 93.5 95.08 93 
 
 
Table 6. Results comparison of maize leaf classification 
Authors Number of classes Sensitivity (%) Specificity (%) Accuracy (%) 
Sibiya & Sumbwanyambe [20] 3 - - 92.85 
Zhang et al. [18] 8 - - 98.9 
Hidayat et al. [21] 3 - - 93.67 
Proposed method 4 93.5 95.08 93 
 
 
4. CONCLUSION 
This study analyzed maize leaf image classification using 7 CNN architectures (AlexNet, VGG16, 
VGG19, ResNet50, ResNet110. GoogleNet, and Inception-V3) and the classification methods (SVM, kNN, 
and Decision Tree). The best classification was generated by AlexNet architecture with SVM. This showed 
that AlexNet and SVM methods were best suited for feature extraction and image classification of maize 
leaves disease. Furthermore, we could increase the percentage of accuracy by adding optimization methods in 
CNN architectures. 
 
 
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Convolutional neural network  for maize leaf disease image classification

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Convolutional neural network for maize leaf disease image classification

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