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Int. J. Fuzzy Log. Intell. Syst. 2017; 17(1): 26-34

Published online March 31, 2017

https://doi.org/10.5391/IJFIS.2017.17.1.26

© The Korean Institute of Intelligent Systems

Plant Leaf Recognition Using a Convolution Neural Network

Wang-Su Jeon1, and Sang-Yong Rhee2

1Department of IT Convergence Engineering, Kyungnam University, Changwon, Korea, 2Department of Computer Engineering, Kyungnam University, Changwon, Korea

Correspondence to :
Sang-Yong Rhee (syrhee@kyungnam.ac.kr)

Received: February 1, 2017; Revised: February 23, 2017; Accepted: March 24, 2017

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

There are hundreds of kinds of trees in the natural ecosystem, and it can be very difficult to distinguish between them. Botanists and those who study plants however, are able to identify the type of tree at a glance by using the characteristics of the leaf. Machine learning is used to automatically classify leaf types. Studied extensively in 2012, this is a rapidly growing field based on deep learning. Deep learning is itself a self-learning technique used on large amounts of data, and recent developments in hardware and big data have made this technique more practical. We propose a method to classify leaves using the CNN model, which is often used when applying deep learning to image processing.

Keywords: Leaf, Classification, Visual system, CNN, GoogleNet

No potential conflict of interest relevant to this article was reported.

Wang-Su Jeon received his B.S. degree in Computer Engineering from Kyungnam University, Changwon, Korea, in 2016, and is currently pursuing an M.S. degree in IT Convergence Engineering at Kyungnam University, Changwon, Korea. His present interests include computer vision, pattern recognition and machine learning.

E-mail: jws2218@naver.com


Sang-Yong Rhee received his B.S. and M.S. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1982 and 1984, respectively, and his Ph.D. degree in Industrial Engineering from Pohang University of Science and Technology, Pohang, Korea. He is currently a professor in the Department of Computer Engineering, Kyungnam University, Changwon, Korea. His research interests include computer vision, augmented reality, neuro-fuzzy, and human-robot interfaces.

E-mail: syrhee@kyungnam.ac.kr


Article

Original Article

Int. J. Fuzzy Log. Intell. Syst. 2017; 17(1): 26-34

Published online March 31, 2017 https://doi.org/10.5391/IJFIS.2017.17.1.26

Copyright © The Korean Institute of Intelligent Systems.

Plant Leaf Recognition Using a Convolution Neural Network

Wang-Su Jeon1, and Sang-Yong Rhee2

1Department of IT Convergence Engineering, Kyungnam University, Changwon, Korea, 2Department of Computer Engineering, Kyungnam University, Changwon, Korea

Correspondence to: Sang-Yong Rhee (syrhee@kyungnam.ac.kr)

Received: February 1, 2017; Revised: February 23, 2017; Accepted: March 24, 2017

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There are hundreds of kinds of trees in the natural ecosystem, and it can be very difficult to distinguish between them. Botanists and those who study plants however, are able to identify the type of tree at a glance by using the characteristics of the leaf. Machine learning is used to automatically classify leaf types. Studied extensively in 2012, this is a rapidly growing field based on deep learning. Deep learning is itself a self-learning technique used on large amounts of data, and recent developments in hardware and big data have made this technique more practical. We propose a method to classify leaves using the CNN model, which is often used when applying deep learning to image processing.

Keywords: Leaf, Classification, Visual system, CNN, GoogleNet

Fig 1.

Figure 1.

System composition.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 2.

Figure 2.

Example of leaf contour extraction.

(a) Input image, (b) gray scale image,

(c) binary image, and (d) contour extraction.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 3.

Figure 3.

Human visual system structure.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 4.

Figure 4.

Basic structure of a convolution neural network.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 5.

Figure 5.

Inception module structure.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 6.

Figure 6.

Factorizing convolution used in the VGGNet model.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 7.

Figure 7.

GoogleNet structure and auxiliary classifier units.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 8.

Figure 8.

Batch normalization method.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 9.

Figure 9.

Leaf image cropping and resize example. (a) Input image, (b) cropping image, (c) 229×229 image.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 10.

Figure 10.

Multi-scale image.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 11.

Figure 11.

Factorizing convolution applied in the inception module.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 12.

Figure 12.

(a) Flavia image dataset and (b) natural leaves.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 13.

Figure 13.

Leaf shapes: (a) lanceolate, (b) oval, (c) acicular, (d) linear, (e) reniform, (f) kidney-shaped, (g) cordate, heart-shaped, and (h) palmate leaf.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 14.

Figure 14.

Color change: (a) input image, (b) discoloration 5%, (c) discoloration 10%, (d) discoloration 30%, (e) discoloration 50%, and (f) discoloration 60%.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Fig 15.

Figure 15.

Leaf damage: (a) damage 5%, (b) damage 10%, (c) damage 15%, and (d) damage 30%.

The International Journal of Fuzzy Logic and Intelligent Systems 2017; 17: 26-34https://doi.org/10.5391/IJFIS.2017.17.1.26

Table 1 . GoogleNet basic structure [Model 1].

Type Filter size / stride Input size
Conv3 × 3 / 2222 × 229
Conv3 × 3 / 1 149 × 149 × 32 
 Conv padded 3 × 3 / 1147 × 147 × 32
Ppool3 × 3 / 2147 × 147 × 64
Conv3 × 3 / 173 × 73 × 64
Conv3 × 3 / 271 × 71 × 80
Conv3 × 3 / 135 × 35 × 192
3×InceptionFigure 10(a)35 × 35 × 288
5×InceptionFigure 10(b)17 × 17 × 768
2×InceptionFigure 10(c)8 × 8 × 1280
Pool8 × 88 × 8 × 2048
LinearLogits1 × 1 × 2048
SoftmaxClassifier1 × 1 × 1000

Conv: convolution..


Table 2 . Modified GoogleNet structure [Model 2].

Type Filter size / stride Input size
Conv3 × 3 / 2222 × 229
Conv3 × 3 / 1 149 × 149 × 32 
 Conv padded 3 × 3 / 1147 × 147 × 32
Pool3 × 3 / 2147 × 147 × 64
Conv3 × 3 / 173 × 73 × 64
Conv3 × 3 / 271 × 71 × 80
Conv3 × 3 / 135 × 35 × 192
5×InceptionFigure 10(a)35 × 35 × 288
5×InceptionFigure 10(b)17 × 17 × 768
2×InceptionFigure 10(c)8 × 8 × 1280
Pool8 × 88 × 8 × 2048
LinearLogits1 × 1 × 2048
SoftmaxClassifier1 × 1 × 1000

Table 3 . Type and number of leaves.

Leaf type Number of images 
Lanceolate568
Oval554
Acicular612
Linear439
Oblong374
 Reniform, kidney-shaped 580
Cordate, heart-shaped379
Palmate leaf361

Table 4 . Model performance evaluation.

 Model 1  Model 2 
Image size229 × 229229 × 229
 Training time 8h 43m9h 18m
Accuracy99.6%99.8%

Table 5 . Accuracy rate (%) in relation to discoloration.

 Model 1  Model 2 
Image in Figure 14(b)99.599.65
Image in Figure 14(c)99.299.3
Image in Figure 14(d)98.899.1
Image in Figure 14(e)98.598.9
Image in Figure 14(f)98.298.6

Table 6 . Accuracy rate (%) in relation to damage.

 Model 1  Model 2 
Image in Figure 15(a)97.498.4
Image in Figure 15(b)96.898
Image in Figure 15(c)96.297.6
Image in Figure 15(d)94.495

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