Sugarcane Diseases Detection using the Improved Grey Wolf Optimization Algorithm with Convolution Neural Network

Davesh Kumar Sharma; Pushpendra Singh; Akash Punhani

doi:10.52756/ijerr.2024.v38.022

Davesh Kumar Sharma Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, U.P., India https://orcid.org/0009-0000-9811-9322
Pushpendra Singh Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, U.P., India https://orcid.org/0000-0003-2531-0999
Akash Punhani Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, U.P., India https://orcid.org/0000-0002-4693-498X

DOI: https://doi.org/10.52756/ijerr.2024.v38.022

Keywords: Convolution Neural Network, Diseases Detection, Grey Wolf Optimization Algorithm, Sugarcane

Abstract

The Indian economy is heavily dependent on agriculture as most people have agriculture as their source of income. Therefore, Indian researchers must focus on the various challenges in this field. As there is huge diversity in the cultivation scenario in the different geographical locations of the country. However, sugarcane is one of the important crops grown in the country's major states. The major challenge other than climate change is the impact of plant diseases on the various crops. Various infections are caused by the different viruses and bacteria that lead to poor crop yield, leading to losses to farmers, which reflects losses to the country. The identification of the diseases is done by due inspection by experts in the fields; however, it is difficult for farmers to contact them, and their availability will be a challenge. Therefore, it is wise enough for the tools to be developed to extract the features from the images of the plant leaves and effectively identify the diseases. In this paper, a solution to the problem of disease identification is proposed with the help of the CNN model, the parameters of which are tuned using the grey wolf optimization neural network. The model based on IGWO is compared with the three other optimization algorithms, GWO, GA and PSO, out of which the IGWO and GA had the competitive result in accuracy comparison. However, the precision was better for PSO and GWO algorithms.

References

Ahmed, A., Darwish, S. M., & Elsherbiny, M. M. (2019). A novel automatic CNN architecture design approach based on genetic algorithm. In Advances in Intelligent Systems and Computing, Springer International Publishing, pp. 473–482. https://doi.org/10.1007/978-3-030-31129-2_43

Chen, J., Jiang, J., Guo, X., & Tan, L. (2020). A self-Adaptive CNN with PSO for bearing fault diagnosis. Systems Science & Control Engineering, 9(1), 11–22. https://doi.org/10.1080/21642583.2020.1860153

Daphal, S. D., & Koli, S. M. (2023). Enhancing sugarcane disease classification with ensemble deep learning: A comparative study with transfer learning techniques. Heliyon, 9(8), e18261. https://doi.org/10.1016/j.heliyon.2023.e18261

Hemalatha, N., Brunda, R., Prakruthi, G., Prabhu, B. V. B., Shukla, A., & Narasipura, O. S. J. (2022). Sugarcane leaf disease detection through deep learning. In Elsevier eBooks: In Deep Learning for Sustainable Agriculture, pp. 297–323. https://doi.org/10.1016/b978-0-323-85214-2.00003-3

Khan, A., Yadav, M. S., & Ahmad, S. (2017). Image processing based disease detection for sugarcane leaves. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4), 497-502.

Komol, M. M. R., Hasan, M. S., & Ali, S. (2023). Sugarcane Diseases identification and detection via machine learning. In Algorithms for intelligent systems, vol. 3. Eds. Bansal, J. C., Uddin, M. S. (Springer Nature Singapore, Singapore), pp.37–51. https://doi.org/10.1007/978-981-99-3754-7_3

Kumar, P. (2021). Sugarcane Disease Detection Model. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 5167-5174.

Kumaran, N., Vadivel, A., & Kumar, S. S. (2018). Recognition of human actions using CNN-GWO: a novel modeling of CNN for enhancement of classification performance. Multimedia Tools and Applications, 77(18), 23115–23147. https://doi.org/10.1007/s11042-017-5591-z

Malik, H. S., Dwivedi, M., Omkar, S. N., Javed, T., Bakey, A., Pala, M. R., & Chakravarthy, A. (2020). Disease recognition in sugarcane crop using deep learning. In Advances in Intelligent Systems and Computing, pp. 189–206. https://doi.org/10.1007/978-981-15-3514-7_17

Militante, S. V., Gerardo, B. D., & Medina, R. P. (2019). Sugarcane Disease Recognition using Deep Learning. 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan, pp. 575-578.

https://doi.org/10.1109/ecice47484.2019.8942690

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Murugeswari, R., Anwar, Z. S., Dhananjeyan, V. R., & Karthik, C. (2022). Automated Sugarcane Disease Detection Using Faster RCNN with an Android Application. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2022, 1-7. https://doi.org/10.1109/icoei53556.2022.9776685

Nadimi-Shahraki, M. H., Taghian, S., & Mirjalili, S. (2021). An improved grey wolf optimizer for solving engineering problems. Expert Systems With Applications, 166, 113917. https://doi.org/10.1016/j.eswa.2020.113917

Narmilan, A., Gonzalez, F., Salgadoe, A. S. A., Sandino, J., & Powell, K. (2022). Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models. Remote Sensing, 14(23), 6137. https://doi.org/10.3390/rs14236137

Özgüven, M. M., & Adem, K. (2019). Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica. A: Statistical Mechanics and its Applications, 535, 122537. https://doi.org/10.1016/j.physa.2019.122537

Srivastava, S., Kumar, P., Mohd, N., Singh, A., & Gill, F. S. (2020). A novel deep learning framework approach for sugarcane disease detection. SN Computer Science, 1(2), 87. https://doi.org/10.1007/s42979-020-0094-9

Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Lv, J. (2020). Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Transactions on Cybernetics, 50(9), 3840–3854. https://doi.org/10.1109/tcyb.2020.2983860

Thilagavathi, K., Kavitha, K., Dhivya, P. R., Arina, S. V. A. J., Sahana, R. C. (2020). Detection of diseases in sugarcane using image processing techniques. Bioscience Biotechnology Research Communications, 13(11), 109–115. https://doi.org/10.21786/bbrc/13.11/24

Upadhye, S. A., Dhanvijay, M. R., & Patil, S. M. (2023). Sugarcane Disease Detection Using CNN-Deep Learning Method: An Indian Perspective. Universal Journal of Agricultural Research, 11(1), 80–97. https://doi.org/10.13189/ujar.2023.110108

Wang, Y., Zhang, H., & Zhang, G. (2019). cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks. Swarm and Evolutionary Computation, 49, 114–123. https://doi.org/10.1016/j.swevo.2019.06.002

Xie, L., & Yuille, A. (2017). Genetic CNN. In Proceedings of the IEEE International Conference on Computer Vision, arXiv (Cornell University). pp. 1379-1388. https://doi.org/10.48550/arxiv.1703.01513