Browse > Article
http://dx.doi.org/10.9717/kmms.2021.24.10.1358

Sweet Persimmons Classification based on a Mixed Two-Step Synthetic Neural Network  

Roh, SeungHee (Dept. of Information and Communications Engineering, Changwon National University)
Park, DongGyu (Dept. of Information and Communications Engineering, Changwon National University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.10, 2021 , pp. 1358-1368 More about this Journal
Abstract
A research on agricultural automation is a main issues to overcome the shortage of labor in Korea. A sweet persimmon farmers need much time and labors for classifying profitable sweet persimmon and ill profitable products. In this paper, we propose a mixed two-step synthetic neural network model for efficiently classifying sweet persimmon images. In this model, we suggested a surface direction classification model and a quality screening model which constructed from image data sets. Also we studied Class Activation Mapping(CAM) for visualization to easily inspect the quality of the classified products. The proposed mixed two-step model showed high performance compared to the simple binary classification model and the multi-class classification model, and it was possible to easily identify the weak parts of the classification in a dataset.
Keywords
Deep Neural Network; Convolutional Neural Network Classifier; Persimmon Image Classification; Class Activation Mapping;
Citations & Related Records
연도 인용수 순위
  • Reference
1 L.G. Nachtigall, R.M. Araujo, and G.R.. Nachtigall, "Classification of Apple Tree Disorders Using Convolutional Neural Networks," Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, pp. 472-476, 2016.
2 K.P. Ferentions, "Deep Learning Models for Plant Disease Detection and Diagnosis," Computers and Electronics in Agriculture, Vol. 145, pp. 311-318, 2018.   DOI
3 K.E. Ko, S. Yang, and I.H. Jang "Real-Time Tomato Ripeness Classification System based on Deep Learning Model for Object Detection," Journal of Institute of Control Robotics and Systems, Vol. 24 No. 11, pp. 999-1004, 2018.   DOI
4 A.S.A. Mettleq, I.M. Dheir, A.A. Elsharif and S.S. Abu-Naser, "Mango Classification Using Deep Learning," International Journal of Academic Engineering Research, Vol. 3 No. 12, pp. 22-29, 2020.
5 J. Li, S. Xie, Z. Chen, H. Liu, and J. Kang and Z. Fan, et al. "A Shallow Convolutional Neural Network for Apple Classification," IEEE Access, Vol. 8, pp. 111683-111692, 2020.   DOI
6 C. Rother, V. Kolmogorov, and A. Blake, "Grab Cut: Interactive Foreground Extraction Using Iterated Graph Cuts", ACM SIGGRAPH, Vol. 23, No. 3, pp. 309-314, 2004.   DOI
7 M. Valeria, E.G. Carlos, A.Z. Laura, M.C. Jose, G. Jorge, and G. Hamurabi, et al. "Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases," Applied Sciences, Vol. 10, Issue 4, ID 1245, 2020, doi:10.3390/app10041245.   DOI
8 S.P. Mohanty, D.P. Hughes, and M. Salathe, "Using Deep Learning for Image-Based Plant Disease Detection," Frontier in Plant Science, Vol. 7, No. 1419, pp. 1-7, 2016.
9 M. Kanae, S. Maria, B. Kohei, T. Kouki, S. Tetsuya, and S. Maya, et al. "Noninvasive Diagnosis of Seedless Fruit Using Deep Learning in Persimmon," The Horticulture Journal Preview, Vol. 90, No. 2, pp. 172-180, 2021.   DOI
10 M. So, C. Han, and H. Kim, "Defect Classification Algorithm of Fruits Using Modified MobileNet," Journal of KIIT, Vol. 18, No. 7, pp. 81-89, 2020.
11 C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna "Rethinking the Inception Architecture for Computer Vision," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.
12 F. Chollet, "Xception: Deep learning with Depthwise Separable Convolutions," arXiv preprint, arXiv:1610.02357, 2016.
13 R. Zhang, and E. Lee, "Human Face Recognition Based on improved CNN Model with Multi-layers," Journal of Korea Multimedia Society, Vol. 24, No. 5, pp 701-708, 2021.   DOI
14 B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, "Learning Deep Features for Discriminative Localization," arXiv preprint, arXiv:1512.04150, 2015.
15 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, and D. Anguelov, et al., "Going Deeper with Convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
16 K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv preprint. arXiv:1409.1556, 2014.
17 A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, and T. Weyand, "Mobile Nets: Efficient Convolutional Neural Networks for Mobile Vision Applications", arXiv preprint, arXiv:1704.04861, 2017.
18 G. Wang, Y. Sun, and J. Wang, "Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning," Journal of Computational Intelligence and Neuroscience, Vol 2017, Article ID 2917536, 2017, https://doi.org/10.1155/2017/2917536.   DOI
19 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
20 H. Ham, D. Kim, J. Chae, S. Lee, Y. Kim, and H.U. Cho, et al. "A Study of Tomato Disease Classification System Based on Deep Learning," The Transactions of the Korean Institute of Electrical Engineers, Vol. 69, No. 2, pp. 349-355, 2020.   DOI
21 J. Park, H. Kim, and K. Kim, "Accessing Impact of DCGAN Image Data Augmentation for CNN based Tomato Disease Classification," Journal of Digital Contents Society, Vol. 21, No. 5, pp. 959-967, May 2020   DOI
22 O.A. Mohammed and S.A. Samy "Classification of Apple Fruits by Deep Learning," International Journal of Academic Engineering Research, Vol. 3 Issue 12, pp. 1-9, 2019.
23 S. Sakib1, Z. Ashrafi, and Md. A.B. Sidique "Implementation of Fruits Recognition Classifier using Convolutional Neural Network Algorithm for Observation of Accuracies for Various Hidden Layers," arXiv preprint, arXiv:1904.00783, 2019.