令和3年度 電気・電子・情報関係学会 四国支部連合大会 講演論文集
2021 Shikoku-Section Joint Convention Record of the Institutes of Electrical and Related Engineers

1-32
Classification of Shiitake Mushrooms
by Using Convolutional Neural Networks
Kazuki SATOKAWA Yoko UWATE Yoshifumi NISHIO( Tokushima University )1. Introduction

3. Results

Mushrooms have high commercial value, such as fruits
have high-end products. Product value is determined byvarious factors such as roundness, thickness, and size. The
quick way to find product value of shiitake mushrooms iswhether the caps is open or not. Currently, people aredeciding whether or not the cap is open. It is necessary toclassify automatically by using AI.
In this study, we improve classification accuracy by using edge detection called the Canny method. We classify
images with edge detection by using convolutional neuralnetworks(CNN)[1]. We aim at emphasizing the difference
between characteristics of shiitake mushrooms.

We compare the learning and test accuracies of 4 data
sets of images or images with edge detection. The first data
set has original images which are 110 images with the open
caps and 110 images with the closed caps. The second data
set has images with edge detection, which are 110 images
with the open caps and 110 images with the close caps.
They are made from the first data set by using the Cannymethod. The threshold for this data set is 60. The third
data set and forth data set have images with edge detection
as well as second data set, and the threshold for these data
set is 50 and 40, respectively.
The all data set are classified whether the caps are open
or not by using the convolutional neural networks which has
4 convolutional layers and 2 pooling layers.
Table 1 summarizes the simulation results. The test accuracy of the classification for the forth data set is 0.94.
This score is higher than the score of the first data set. It isconsidered that the difference in caps is more emphasized.

2. Proposed system
We propose the method to classify the caps of shiitake
mushrooms.
First, we process images with edge detection by using
the Canny method in Fig. 1. We create 3 types of images
with different threshold values. The data sets have each 220
images with edge detection. They are 110 images with theopen cap and 110 images with the closed cap.
Second, we classify images with the standard CNN with
4 convolutional layers and 2 pooling layers.

Table 1: Test accuracies.
data set
original imagesedge detection (threshold is 60)
edge detection (threshold is 50)
edge detection (threshold is 40)

Test accuracy
0.91
0.65
0.69
0.94

4. Conclusion(a)

In this study, we have investigated comparison of theclassification whether the caps of Shiitake mushrooms is
open or not with the 4 data sets. The first data set hasoriginal images. The other data sets has images with edge
detection using the different threshold values.
From these simulation results, we confirmed that theproposed system is effective for classifying caps of shiitake
mushroom. However, the test accuracies of the second data
set and third data set are lower than the test accuracy of
the first data set, so we will investigate the the effects ofdifference of the threshold.

(b)

References
(c)

(d)

Figure 1: Original images and images with edge detection.
(a) A original image of a open cap.
(b) A original image of a closed cap.
(c) A image with edge detection of a open cap.
(d) A image with edge detection of a closed cap.

[1] A. Krizhevsky, I. Sutskever and G. Hinton, “ImageNet
Classification with Deep Convolutional Neural Networks”, Neural Information Processing Systems(NIPS),
pp. 1097-1105, Dec. 2012.

Acknowledgement
This work was partially supported by TOKUSHINELECTRIC INDUSTRIES CO.,LTD.

–32–