The week's pick
Random Articles
Reseach Article
Comparative Analysis of Fine-tuning Multiple Pre- Trained Convolutional Neural Network (CNN) Models for Oryza Sativa Disease Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 34 |
| Year of Publication: 2023 |
| Authors: Roky Das, Iqbal Ahmed |
10.5120/ijca2023923117
|
Roky Das, Iqbal Ahmed . Comparative Analysis of Fine-tuning Multiple Pre- Trained Convolutional Neural Network (CNN) Models for Oryza Sativa Disease Detection. International Journal of Computer Applications. 185, 34 ( Sep 2023), 9-16. DOI=10.5120/ijca2023923117
Abstract
Rice is the staple food in Bangladesh. Every year many crops are lost because of the disease of the rice plant. Our farmers are facing a great loss every year as well as rice productivity is declining. Some diseased and healthy leaf images of rice plants have been collected. The images have been classified into four classes. The classes are Brown Spot, Hispa, Leaf Blast diseased leaves, and Healthy leaves, usually known as Oryza Saiva diseases of rice. Deep learning is very common and popular to analyze and make predictions from these kind of image data. In this research, the CNN (Convolutional Neural Network) models have been used to predict the classes. Among various CNN models, VGG16, MobileNet, ResNet50, and DenseNet121 pre-trained models have been applied. Among those pre-trained models, the highest accuracy 95.3% has been found from the VGG16 model. In addition, an Android mobile app has been developed using the highest performant VGG16 trained model. Using this mobile app, farmers will be able to upload a photo and predict the disease of that rice plant or not in a convenient way.
References
- P. S. Thakur, T. Sheorey, and A. Ojha, “VGG-ICNN: A Lightweight CNN model for crop disease identification,” Multimed Tools Appl, vol. 82, no. 1, pp. 497–520, 2023.
- H. Ajra, M. K. Nahar, L. Sarkar, and M. S. Islam, “Disease detection of plant leaf using image processing and cnn with preventive measures,” in 2020 Emerging Technology in Computing, Communication and Electronics (ETCCE), 2020, pp. 1–6.
- E. L. Mique Jr and T. D. Palaoag, “Rice pest and disease detection using convolutional neural network,” in Proceedings of the 1st International Conference on Information Science and Systems, 2018, pp. 147–151.
- P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Rice false smut detection based on faster R-CNN,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 19, no. 3, pp. 1590–1595, 2020.
- Z. Saeed, A. Raza, A. H. Qureshi, and M. H. Yousaf, “A multi-crop disease detection and classification approach using CNN,” in 2021 International Conference on Robotics and Automation in Industry (ICRAI), 2021, pp. 1–6.
- S. Ghosal and K. Sarkar, “Rice leaf diseases classification using CNN with transfer learning,” in 2020 IEEE Calcutta Conference (CALCON), 2020, pp. 230–236.
- G. Latif, S. E. Abdelhamid, R. E. Mallouhy, J. Alghazo, and Z. A. Kazimi, “Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model,” Plants, vol. 11, no. 17, p. 2230, 2022.
- K. P. Panigrahi, A. K. Sahoo, and H. Das, “A cnn approach for corn leaves disease detection to support digital agricultural system,” in 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 2020, pp. 678–683.
- P. A. H. Vardhini, S. Asritha, and Y. S. Devi, “Efficient disease detection of paddy crop using cnn,” in 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 2020, pp. 116–119.
- P. Sharma, Y. P. S. Berwal, and W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation,” Information Processing in Agriculture, vol. 7, no. 4, pp. 566–574, 2020.
- B. S. Bari et al., “A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework,” PeerJ Comput Sci, vol. 7, p. e432, 2021.
- C. R. Rahman et al., “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosyst Eng, vol. 194, pp. 112–120, 2020.
- D. Mohapatra, J. Tripathy, and T. K. Patra, “Rice disease detection and monitoring using CNN and naive Bayes classification,” in Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020), 2021, pp. 11–29.
- G. Zhou, W. Zhang, A. Chen, M. He, and X. Ma, “Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion,” IEEE access, vol. 7, pp. 143190–143206, 2019.
Index Terms
Keywords