Browse > Article
http://dx.doi.org/10.9717/kmms.2022.25.11.1653

A Hierarchical Deep Convolutional Neural Network for Crop Species and Diseases Classification  

Borin, Min (School of Computer Science, Chungbuk National University)
Rah, HyungChul (Research Institute of Veterinry Medicine, Chungbuk National University)
Yoo, Kwan-Hee (School of Computer Science, Chungbuk National University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.11, 2022 , pp. 1653-1671 More about this Journal
Abstract
Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.
Keywords
Crop Classification; Disease Detection; Disease Classification; Deep Convolutional Neural Network;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Liu, S. Yang, Y. Cheng, and Z. Song, "Plant Leaf Classification Based on Deep Learning," 2018 Chinese Automation Congress (CAC), pp. 3165-3169, 2018.
2 E. Goceri, "Image Augmentation for Deep Learning Based Lesion Classification from Skin Images," 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), pp. 144-148, 2020.
3 J. Shijie, W. Ping, J. Peiyi, and H. Siping, "Research on Data Augmentation for Image Classification Based on Convolution Neural Networks," 2017 Chinese Automation Congress (CAC), pp. 4165-4170, 2017.
4 J. Lemley, S. Bazrafkan, and P. Corcoran, "Smart Augmentation Learning an Optimal Data Augmentation Strategy," IEEE Access, Vol. 5, pp. 5858-5869, 2017.   DOI
5 E. Hirani, V. Magotra, J. Jain, and P. Bide, "Plant Disease Detection Using Deep Learning," 2021 6th International Conference for Convergence in Technology (I2CT), pp. 1-4, 2021.
6 M. Mirza and S. Osindero, "Conditional Generative Adversarial Nets," arXiv Preprint, arXiv:1411.1784, 2014.
7 K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," Proceedings of the IEEE International Conference on Computer Vision, pp. 2961-2969, 2017.
8 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., "Going Deeper with Convolutions," arXiv Preprint, arXiv:1409. 4842, 2014.
9 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.
10 J.A. Pandian, G. Geetharamani, and B. Annette, "Data Augmentation on Plant Leaf Disease Image Dataset Using Image Manipulation and Deep Learning Techniques," 2019 IEEE 9th International Conference on Advanced Computing (IACC), pp. 199-204, 2019.
11 H. Ajra, M.K. Nahar, L. Sarkar, and M.S. Islam, "Disease Detection of Plant Leaf using Image Processing and CNN with Preventive Measures," 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), pp. 1-6, 2020.
12 Z. Husin, A. Aziz, A. Shakaff, and R. Farook, "Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques," Proceedings of the 3rd International Conference on Intelligent Systems Modeling and Simulation, pp. 291-296, 2012.
13 P.-Y. Wang, C.-T. Chen, J.-W. Su, T.-Y. Wang, and S.-H. Huang, "Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis along with Joint Self-Attention Mechanism," IEEE Access, Vol. 9, pp. 55244-55259, 2021.   DOI
14 M.-K. Kim, "Tomato Crop Disease Classification Using an Ensemble Approach Based on a Deep Neural Network," Journal of Korea Multimedia Society, Vol. 23, pp. 1250-1257, 2020.   DOI
15 T. Youwen, L. Tianlai, and N. Yan, "The Recognition of Cucumber Disease Based on Image Processing and Support Vector Machine," Proceedings of the Congress on Image and Signal Processing, pp. 262-267, 2008.
16 J. Zhang, Y. Rao, C. Man, Z. Jiang, and S. Li, "Identification of Cucumber Leaf Diseases Using Deep Learning and Small Sample Size for Agricultural Internet of Things," International Journal of Distributed Sensor Networks, Vol. 17, Issue 4, 2021.
17 M. Hammami, D. Friboulet, and R. Kechichian, "Cycle GAN-based Data Augmentation for Multi-Organ Detection in CT Images via Yolo," 2020 IEEE International Conference on Image Processing (ICIP ), pp. 390-393, 2020.
18 X. Zhang, Z. Wang, D. Liu, Q. Lin, and Q. Ling, "Deep Adversarial Data Augmentation for Extremely Low Data Regimes," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 31, pp. 15-28, 2020.
19 S.P. Mohanty, D.P. Hughes, and M. Salath, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, Vol. 7, p. 1419, 2016.   DOI
20 G. Geetharamani and A. Pandian, "Identification of Plant Leaf Diseases Using a Nine-Layer Deep Convolutional Neural Network," Computers & Electrical Engineering, Vol. 76, pp. 323-338, 2019.   DOI
21 H.-C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, et al., "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," IEEE Transactions on Medical Imaging, Vol. 35, pp. 1285-1298, 2016.   DOI
22 S. Roh and D. Park, "Sweet Persimmons Classification Based on a Mixed Two-Step Synthetic Neural Network," Journal of Korea Multimedia Society, Vol. 24, pp. 1358-1368, 2021.   DOI
23 P. Isola, J.-Y. Zhu, T. Zhou, and A.A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125-1134, 2017.
24 R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014.
25 K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv Preprint, arXiv: 1409.1556, 2014.
26 D. Roy, P. Panda, and K. Roy, "Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning," Neural Networks, Vol. 121, pp. 148-160, 2020.   DOI
27 H. Nazki, S. Yoon, A. Fuentes, and D.S. Park, "Unsupervised Image Translation Using Adversarial Networks for Improved Plant Disease Recognition," Computers and Electronics in Agriculture, Vol. 168, p. 105117, 2020.   DOI
28 Y. Fang, C. Zhang, C. Huang, L. Liu, and Y. Yang, "Phishing Email Detection Using Improved RCNN Model with Multilevel Vectors and Attention Mechanism," IEEE Access, Vol. 7, pp. 56329-56340, 2019.   DOI
29 K. Jo, S. Jung, and C. Sim, "A Study of Shiitake Disease and Pest Image Analysis Based on Deep Learning," Journal of Korea Multimedia Society, Vol. 23, pp. 50-57, 2020.   DOI