Bell Pepper Leaf Disease Classification Using Fine-Tuned Transfer Learning
Yuris Akhalifi (1*), Agus Subekti (2) (1) Universitas Bina Sarana Informatika - Indonesia - [ http://yurisalkhalifi.com/ ]
(2) Research Center for Telecommunications, National Research and Innovation Agency - Indonesia
(*) Corresponding Author
Received: March 31, 2023; Revised: August 07, 2023
Accepted: August 08, 2023; Published: August 31, 2023
Abstract
Leaf diseases of plants are common worldwide. Using image processing, farmers could spot diseases in pepper plants more rapidly and get advice from plant disease experts. In this paper, researchers developed a Transfer Learning classification model for bell pepper leaf disease, with the Transfer Learning model trained on images of healthy and diseased bell pepper leaves. Classification of healthy and diseased bell pepper leaves has been carried out, and fine-tuned Transfer Learning has been applied using several pre-trained CNN models. To achieve the best outcome, four pre-trained models, including MobileNet, VGG16, ResNetV250, and DenseNet121, and three Fully Connected (FC) layer architectures were tested. The Fully Connected (FC) layer with four Transfer Learning architectures achieved the best accuracy value of 99.33% on DenseNet121 architecture with one layer and Cohen’s Kappa value of 0.9865.
http://dx.doi.org/10.55981/jet.546 Keywords
References
L. M. Anaya-Esparza, Z. V. de la Mora, O. Vázquez-Paulino, F. Ascencio, and A. Villarruel-López, “Bell peppers (capsicum annum l.) losses and wastes: source for food and pharmaceutical applications,” Molecules, vol. 26, no. 17, Sep. 2021, Art no. 5341, doi: 10.3390/MOLECULES26175341. Crossref
Y. González-García et al., “Effect of three nanoparticles (Se, Si and Cu) on the bioactive compounds of bell pepper fruits under saline stress,” Plants, vol. 10, no. 2, 2021, Art no. 217, doi: 10.3390/plants10020217. Crossref
C. Krasnow and C. Ziv, “Non-chemical approaches to control postharvest gray mold disease in bell peppers,” Agronomy, vol. 12, no. 1, Jan. 2022, Art no. 216, doi: 10.3390/agronomy12010216. Crossref
H. P. Susetyo, “Organisme pengganggu tanaman (opt) pada tanaman paprika dan teknik pengendalian,” Holtukultura.com. http://hortikultura.pertanian.go.id/?p=2068 (accessed Jan. 15, 2021).
M. Chandrasekaran, M. Paramasivan, and S. C. Chun, “Bacillus subtilis CBR05 induces vitamin B6 biosynthesis in tomato through the de novo pathway in contributing disease resistance against Xanthomonas campestris pv. vesicatoria,” Sci. Rep., vol. 9, 2019, Art no. 6495, doi: 10.1038/s41598-019-41888-6. Crossref
E. O. Hassan and M. A. Zyton, “Management of bacterial spot of pepper caused by Xanthomonas campestris pv. vesicatoria,” American J. Biosci. Bioeng., vol. 5, no. 1, pp. 41-49, 2017, doi: 10.11648/j.bio.20170501.17. Crossref
J. Hausner, N. Hartmann, M. Jordan, and D. Büttner, “The predicted lytic transglycosylase HpaH from Xanthomonas campestris pv. vesicatoria associates with the type III secretion system and promotes effector protein translocation,” Infect. Immun., vol. 85, no. 2, 2017, Art no. e00788-16, doi: 10.1128/IAI.00788-16. Crossref
F. Mahamud et al., “Bell pepper leaf disease classification using convolutional neural network,” Int. Conf. Intell. Comput. Optimization 2022. Lecture Notes Netw. Syst., vol. 569, pp. 75–86, 2023, doi: 10.1007/978-3-031-19958-5_8. Crossref
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-net: efficient channel attention for deep convolutional neural networks,” in 2020 IEEE/The Computer Vision Foundation Conf. Computer Vision and Pattern Recognition, Jun. 2020, pp. 11531–11539, doi: 10.1109/CVPR42600.2020.01155. Crossref
Y.-D. Zhang, S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S.-H. Wang, “Improved breast cancer classification through combining graph convolutional network and convolutional neural network,” Inf. Process. Manag., vol. 58, no. 2, 2021, Art no. 102439, doi: 10.1016/j.ipm.2020.102439. Crossref
A. Subeesh et al., “Deep convolutional neural network models for weed detection in polyhouse grown bell peppers,” Artif. Intell. Agriculture, vol. 6, pp. 47–54, 2022, doi: 10.1016/j.aiia.2022.01.002. Crossref
P. K. Chaitanya and K. Yasudha, “Image based plant disease detection using convolution neural networks algorithm,” Int. J. Innovative Sci. Res. Technol., vol. 5, no. 5, pp. 331–334, 2020, Accessed: Aug. 22, 2023. [Online]. Available: https://www.ijisrt.com/image-based-plant-disease-detection-using-convolution-neural-networks-algorithm.
V. Singh and A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques,” Inf. Process. Agriculture, vol. 4, no. 1, pp. 41–49, 2017, doi: 10.1016/j.inpa.2016.10.005. Crossref
R. Geirhos et al., “Shortcut learning in deep neural networks,” Nat. Mach. Intell., vol. 2, no. 11, pp. 665–673, Nov. 2020, doi: 10.1038/s42256-020-00257-z. Crossref
M. Jhuria, A. Kumar, and R. Borse, “Image processing for smart farming: detection of disease and fruit grading,” 2013 IEEE 2nd Int. Conf. Image Inf. Process., 2013, pp. 521–526.
M. Bhagat, D. Kumar, R. Mahmood, B. Pati, and M. Kumar, “Bell pepper leaf disease classification using CNN,” 2nd Int. Conf. Data, Engineering and Applications, 2020, doi: 10.1109/IDEA49133.2020.9170728. Crossref
Y. Kurmi, S. Gangwar, D. Agrawal, S. Kumar, and H. S. Srivastava, “Leaf image analysis-based crop diseases classification,” Signal Image Video Process., vol. 15, pp. 589–597, 2021, doi: 10.1007/s11760-020-01780-7. Crossref
PlantVillage dataset|Kaggle2, T. O. Emmanuel, 2019. [Online]. Available: https://www.kaggle.com/emmarex/plantdisease (accessed Jan. 15, 2021).
T. Rahman et al., “Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray,” Appl. Sci., vol. 10, no. 9, May 2020, Art no. 3233, doi: 10.3390/app10093233. Crossref
M. A. H. Akhand, S. Roy, N. Siddique, M. A. S. Kamal, and T. Shimamura, “Facial emotion recognition using transfer learning in the deep CNN,” Electron., vol. 10, no. 9, May 2021, Art no. 1036, doi: 10.3390/electronics10091036. Crossref
I. Deep Mastan and S. Raman, “DeepCFL: deep contextual features learning from a single image,” in Proc. 2021 IEEE Winter Conf. Appl. Comput. Vision, 2021, pp. 2896–2905. doi: 10.1109/WACV48630.2021.00294. Crossref
I. Z. Mukti and D. Biswas, “Transfer learning based plant diseases detection using resnet50,” in 2019 4th Int. Conf. Electrical Information and Communication Technology, Dec. 2019. doi: 10.1109/EICT48899.2019.9068805. Crossref
E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, 2019, doi: 10.1016/j.compag.2018.03.032. Crossref
R. A. Welikala et al., “Fine-tuning deep learning architectures for early detection of oral cancer,” in 2020 Int. Symp. Mathematical and Computational Oncology. Lecture Notes Comp. Sci., vol. 12508, pp. 25–31, 2020, doi: 10.1007/978-3-030-64511-3_3/FIGURES/3. Crossref
S. Aggarwal, S. Gupta, A. Alhudhaif, D. Koundal, R. Gupta, and K. Polat, “Automated covid-19 detection in chest X-ray images using fine-tuned deep learning architectures,” Expert Syst., vol. 39, no. 3, Mar. 2022, Art no. e12749, doi: 10.1111/exsy.12749. Crossref
A. B. Mutiara, “Implementasi deep learning: matlab dan python-keras-tensorflow,” Depok, 2020. Accessed: Jul. 21, 2021. [Online]. Available: https://mooc.aptikom.or.id/mod/resource/view.php?id=1095
A. G. Howard et al., “MobileNets: efficient convolutional neural networks for mobile vision applications,” Apr. 2017. Accessed: Aug. 22, 2023. [Online]. Available: https://arxiv.org/abs/1704.04861v1.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. 3rd Int. Conf. Learning Representations 2015, 2015. Accessed: Aug. 22, 2023. [Online]. Available: https://arxiv.org/abs/1409.1556v6.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 2016, pp. 770–778, doi: 10.1109/CVPR.2016.90. Crossref
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. 30th IEEE Conf. Computer Vision and Pattern Recognition 2017, 2017, pp. 2261–2269, doi: 10.1109/CVPR.2017.243. Crossref
C. El Morr, M. Jammal, H. Ali-Hassan, and W. El-Hallak, “Data preprocessing,” in Machine Learning for Practical Decision Making: A Multidisciplinary Perspective with Applications from Healthcare, Engineering and Business Analytics, C. El Morr, M. Jammal, H. Ali-Hassan, and W. EI-Hallak, Eds., Cham: Springer International Publishing, 2022, pp. 117–163, doi: 10.1007/978-3-031-16990-8_4. Crossref
T. Rahman et al., “Exploring the effect of image enhancement techniques on covid-19 detection using chest X-ray images,” Comput. Biol. Med., vol. 132, 2021, Art no. 104319, doi: 10.1016/j.compbiomed.2021.104319. Crossref
X. Wu, S. Lv, L. Zang, J. Han, and S. Hu, “Conditional bert contextual augmentation,” in Int. Conf. Computational Science 2019. Lecture Notes Comput. Sci., 2019, vol. 11539, pp. 84–95. doi: 10.1007/978-3-030-22747-0_7/TABLES/4. Crossref
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J. Big Data, vol. 6, Dec. 2019, Art no. 60, doi: 10.1186/s40537-019-0197-0. Crossref
D. M. Hibban and W. F. Al Maki, “Classification of ornamental betta fish using convolutional neural network method and grabcut segmentation,” in 2021 Int. Conf. Data Science and Its Applications, 2021, pp. 102–109, doi: 10.1109/ICoDSA53588.2021.9617213. Crossref
C. Ha, V.-D. Tran, L. Ngo Van, and K. Than, “Eliminating overfitting of probabilistic topic models on short and noisy text: the role of dropout,” Int. J. Approximate Reasoning, vol. 112, pp. 85–104, 2019, doi: 10.1016/j.ijar.2019.05.010. Crossref
X. Ying, “An overview of overfitting and its solutions,” J. Phys. Conf. Ser., vol. 1168, no. 2, Feb. 2019, Art no. 022022, doi: 10.1088/1742-6596/1168/2/022022. Crossref
A. T. Putra, K. Usman, and S. Saidah, “Webinar student presence system based on regional convolutional neural network using face recognition,” Jurnal Teknik Informatika, vol. 2, no. 2, pp. 109–118, Mar. 2021, doi: 10.20884/1.jutif.2021.2.2.82. Crossref
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