single-jc.php

JACIII Vol.27 No.3 pp. 333-339
doi: 10.20965/jaciii.2023.p0333
(2023)

Research Paper:

Non-Destructive Classification of Paddy Rice Leaf Disease Infected by Bacterial and Fungal Species Using Vision-Based Deep Learning

Amir A. Bracino*,† ORCID Icon, Danielle Grace D. Evangelista** ORCID Icon, Ronnie S. Concepcion II* ORCID Icon, Elmer P. Dadios* ORCID Icon, and Ryan Rhay P. Vicerra* ORCID Icon

*Department of Manufacturing Engineering and Management Department, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

**Department of Chemical Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Received:
April 22, 2022
Accepted:
July 10, 2022
Published:
May 20, 2023
Keywords:
deep learning, EfficientNet-b0, MobileNet-v2, paddy rice leaf diseases, Places365-GoogLeNet
Abstract

Rice is a plant with rounded hollow articulated culms, flat, well-attached leaf blades, and terminal spikes. Its cultivation and consumption shape the culture, diet, and economy of different groups, especially in Asia. However, farmers suffer great financial losses each year due to rice disease. Therefore, the identification and classification of rice diseases are very important. Prompt, timely, and accurate disease diagnosis prevents product loss and improves crop quality. This study focuses on the classification of whether rice paddy leaf is normal or has a disease (one of the following: bacterial leaf blight (BLB), bacterial leaf streaks (BLS), bacterial panicle blight (BPB): heart, downy mildew, hispa, and rice tungro disease (RTD)) using deep learning-based algorithms such as EfficientNet-b0, MobileNet-v2, and Places365-GoogLeNet. The best model for this simulation was found to be EfficientNet-b0 with an average accuracy of 97.74%.

Cite this article as:
A. Bracino, D. Evangelista, R. Concepcion II, E. Dadios, and R. Vicerra, “Non-Destructive Classification of Paddy Rice Leaf Disease Infected by Bacterial and Fungal Species Using Vision-Based Deep Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 333-339, 2023.
Data files:
References
  1. [1] B. S. Anami, N. N. Malvade, and S. Palaiah, “Classification of yield affecting biotic and abiotic paddy crop stresses using field images,” Inf. Process. Agric, Vol.7, No.2, pp. 272-285, 2020. https://doi.org/10.1016/j.inpa.2019.08.005
  2. [2] T.-T. Chang and E. A. Bardenas, “The Morphology and Varietal Characteristics of the Rice Plant,” Int. Rice Research Institute, p. 40, 1965.
  3. [3] Philippine Statistics Authority, “Palay Production in the Philippines,” Quezon City, 2021.
  4. [4] V. K. Shrivastava and M. K. Pradhan, “Rice plant disease classification using color features: A machine learning paradigm,” J. Plant Pathol., Vol.103, No.1, pp. 17-26, 2021. https://doi.org/10.1007/s42161-020-00683-3
  5. [5] Y. Zhang, H. Zhang, and Z. Tian, “The Application of Gaussian Process Regression in State of Health Prediction of Lithium Ion Batteries,” Proc. 2018 IEEE 3rd Adv. Inf. Technol. Electron. Autom. Control Conf. (IAEAC 2018), pp. 515-519, 2018. https://doi.org/10.1109/IAEAC.2018.8577822
  6. [6] S. Lauguico, R. Concepcion, R. R. Tobias, A. Bandala, R. R. Vicerra, and E. Dadios, “Grape leaf multi-disease detection with confidence value using transfer learning integrated to regions with convolutional neural networks,” 2020 IEEE Reg. 10 Conf. (TENCON), pp. 767-772, 2020. https://doi.org/10.1109/TENCON50793.2020.9293866
  7. [7] S. A. H. Naqvi, “Bacterial Leaf Blight of Rice: An Overview of Epidemiology and Management with Special Reference to Indian Sub-Continent,” Pakistan J. Agric. Res., Vol.32, No.2, 2019. https://doi.org/10.17582/journal.pjar/2019/32.2.359.380
  8. [8] R. A. Cernadas et al., “Code-Assisted Discovery of TAL Effector Targets in Bacterial Leaf Streak of Rice Reveals Contrast with Bacterial Blight and a Novel Susceptibility Gene,” PLoS Pathog., Vol.10, No.2, 2014. https://doi.org/10.1371/journal.ppat.1003972
  9. [9] S. S. Zarbafi and J. H. Ham, “An overview of rice QTLs associated with disease resistance to three major rice diseases: Blast, sheath blight, and bacterial panicle blight,” Agronomy, Vol.9, No.4, 2019. https://doi.org/10.3390/agronomy9040177
  10. [10] S. A. Shahriar, A. A. Imtiaz, M. B. Hossain, A. Husna, and M. N. K. Eaty, “Review: Rice Blast Disease,” Annu. Res. Rev. Biol., Vol.35, No.1, pp. 50-64, 2020. https://doi.org/10.9734/arrb/2020/v35i130180
  11. [11] S. Sunder, S. Ram, and R. Agarwal, “Brown spot of rice: An overview,” Indian Phytopathol., Vol.67, No.3, pp. 201-215, 2014.
  12. [12] S. Omprakash, M. Venkataiah, and S. Laxman, “Comparative Efficacy of Different Granular Insecticides Against Yellow Rice Stem Borer Scirpophaga Incertulas (Walker) Under Field Condition,” J. Entomol. Zool. Stud., Vol.5, No.5, pp. 1126-1129, 2017.
  13. [13] Tamil Nadu Agricultural University, “Crop Protection: Pest of paddy,” 2020.
  14. [14] S. H. Ou, “Rice Diseases 2nd Ed.,” Commonwealth Mycological Institute, England, 1985.
  15. [15] P. Nagdev, M. Kumari, and J. Ganguli, “Incidence and management of rice hispa, Dicladispa armigera (Oliver) through Bio Intensive Pest Management (BIPM) at Raipur, Chhattisgarh,” The Pharma Innovation J., Vol.11, No.3, pp. 1387-1389, 2022.
  16. [16] A. Macovei et al., “Novel alleles of rice eIF4G generated by CRISPR/Cas9-targeted mutagenesis confer resistance to rice tungro spherical virus,” Plant Biotechnol. J., Vol.16, No.11, pp. 1918-1927, 2018. https://doi.org/10.1111/pbi.12927
  17. [17] K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” arXiv:1511.08458, 2015.
  18. [18] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. (ICET 2017), 2018. https://doi.org/10.1109/ICEngTechnol.2017.8308186
  19. [19] P. Kaur, B. S. Khehra, and A. P. Singh, “Evaluation of Base Networks for Object Classification and Detection,” Int. J. Adv. Res. Eng. Technol., Vol.11, No.12, pp. 3132-3141, 2020. https://doi.org/10.34218/IJARET.11.12.2020.295
  20. [20] Z.-W. Yuan and J. Zhang, “Feature extraction and image retrieval based on AlexNet,” Eighth Int. Conf. Digit. Image Process (ICDIP 2016), Vol.10033, Article No.100330E, 2016. https://doi.org/10.1117/12.2243849
  21. [21] Jahandad, S. M. Sam, K. Kamardin, N. N. Amir Sjarif, and N. Mohamed, “Offline signature verification using deep learning convolutional neural network (CNN) architectures GoogLeNet inception-v1 and inception-v3,” Procedia Comput. Sci., Vol.161, pp. 475-483, 2019. https://doi.org/10.1016/j.procs.2019.11.147
  22. [22] K. Zhang, Y. Guo, X. Wang, J. Yuan, and Q. Ding, “Multiple feature reweight DenseNet for image classification,” IEEE Access, Vol.7, pp. 9872-9880, 2019. https://doi.org/10.1109/ACCESS.2018.2890127
  23. [23] H. Qassim, A. Verma, and D. Feinzimer, “Compressed residual-VGG16 CNN model for big data places image recognition,” 2018 IEEE 8th Annu. Comput. Commun. Work. Conf. (CCWC 2018), pp. 169-175, 2018. https://doi.org/10.1109/CCWC.2018.8301729
  24. [24] H. Amin and A. Darwish, “End-to-End Deep Learning Model for Corn Leaf Disease Classification,” IEEE Access, Vol.10, pp. 31103-31115, 2022. https://doi.org/10.1109/ACCESS.2022.3159678
  25. [25] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4510-4520, 2018. https://doi.org/10.1109/CVPR.2018.00474
  26. [26] Q. Xiang, G. Zhang, X. Wang, J. Lai, R. Li, and Q. Hu, “Fruit image classification based on Mobilenetv2 with transfer learning technique,” ACM Int. Conf. Proc. Ser., 2019. https://doi.org/10.1145/3331453.3361658
  27. [27] C. Szegedy et al., “Going Deeper with Convolutions,” 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015. https://doi.org/10.1109/CVPR.2015.7298594
  28. [28] C. H. Mendigoria, R. Concepcion, A. Bandala, O. J. Alajas, H. Aquino, and E. Dadios, “OryzaNet: Leaf Quality Assessment of Oryza sativa Using Hybrid Machine Learning and Deep Neural Network,” 2021 IEEE 13th Int. Conf. Humanoid, Nanotechnology, Inf. Technol. Commun. Control. Environ. Manag. (HNICEM 2021), 2021. https://doi.org/10.1109/HNICEM54116.2021.9731957

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 23, 2024