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Tomato Leaf Diseases Classification using Convolutional Neural Networks with Transfer Learning Resnet-50
Corresponding Author(s) : Muslih
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 2, May 2024
Abstract
This research delves into the critical domain of Tomato Leaf Disease classification using advanced machine learning techniques. Specifically, a comparative evaluation was conducted between a Base CNN model devoid of ResNet-50 integration and a Proposed Method harnessing the capabilities of ResNet-50. The results elucidated a notable enhancement in performance metrics when leveraging ResNet-50, with the Proposed Method consistently achieving exceptional accuracy scores of 99.96%, 99.98%, and 99.96% across data splits of 90:10, 80:20, and 70:30, respectively. Furthermore, the ResNet-50 integration significantly augmented key metrics, including recall, precision, and F1-Score, thereby accentuating its pivotal role in enhancing sensitivity and positive predictive value for tomato leaf disease classification. As for prospective research trajectories, this study highlights potential avenues for refinement, encompassing the exploration of ensemble techniques amalgamating diverse architectural frameworks, advanced data augmentation methodologies, and broader disease classification scopes. Collectively, this research underscores the transformative potential of ResNet-50 in agricultural diagnostics, advocating for continued exploration and innovation to fortify global food security and sustainable farming practices. Future research could explore ensemble techniques, advanced data augmentation, broader disease classification scopes, and interdisciplinary collaborations to develop comprehensive diagnostic tools for sustainable farming practices and global food security.
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- S. V. Mahadevkar et al., “A Review on Machine Learning Styles in Computer Vision - Techniques and Future Directions,” IEEE Access, vol. 10. Institute of Electrical and Electronics Engineers Inc., pp. 107293–107329, 2022. https://doi.org/10.1109/ACCESS.2022.3209825
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- M. M. I. Al-Ghiffary, C. A. Sari, E. H. Rachmawanto, N. M. Yacoob, N. R. D. Cahyo, and R. R. Ali, “Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture,” Advance Sustainable Science Engineering and Technology, vol. 5, no. 3, p. 0230304, Oct. 2023. https://doi.org/10.26877/asset.v5i3.17017
- A. Deshpande, V. V. Estrela, and P. Patavardhan, “The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50,” Neuroscience Informatics, vol. 1, no. 4, p. 100013, Dec. 2021. https://doi.org/10.1016/j.neuri.2021.100013
- S. Showkat and S. Qureshi, “Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia,” Chemometrics and Intelligent Laboratory Systems, vol. 224, May 2022. https://doi.org/10.1016/j.chemolab.2022.104534
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References
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D. Bhatt et al., “Cnn variants for computer vision: History, architecture, application, challenges and future scope,” Electronics (Switzerland), vol. 10, no. 20. MDPI, Oct. 01, 2021. https://doi.org/10.3390/electronics10202470
D. Fajri Riesaputri, C. Atika Sari, D. R. Ignatius Moses Setiadi, and E. Hari Rachmawanto, “Classification of Breast Cancer using PNN Classifier based on GLCM Feature Extraction and GMM Segmentation,” in Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020, Institute of Electrical and Electronics Engineers Inc., Sep. 2020, pp. 83–87. https://doi.org/10.1109/iSemantic50169.2020.9234207
N. R. D. Cahyo, C. A. Sari, E. H. Rachmawanto, C. Jatmoko, R. R. A. Al-Jawry, and M. A. Alkhafaji, “A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification,” in 2023 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, Sep. 2023, pp. 358–363. https://doi.org/10.1109/iSemantic59612.2023.10295336
T. Fahey et al., “Active and passive electro-optical sensors for health assessment in food crops,” Sensors (Switzerland), vol. 21, no. 1. MDPI AG, pp. 1–40, Jan. 01, 2021. https://doi.org/10.3390/s21010171
D. P. Roberts, N. M. Short, J. Sill, D. K. Lakshman, X. Hu, and M. Buser, “Precision agriculture and geospatial techniques for sustainable disease control,” Indian Phytopathology, vol. 74, no. 2. Springer, pp. 287–305, Jun. 01, 2021. https://doi.org/10.1007/s42360-021-00334-2
A. S. Paymode and V. B. Malode, “Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG,” Artificial Intelligence in Agriculture, vol. 6, pp. 23–33, Jan. 2022. https://doi.org/10.1016/j.aiia.2021.12.002
C. Umam, D. Andi Krismawan, and R. Raad Ali, “CNN for Image Identification of Hiragana Based on Pattern Recognition,” 2021.
A. S. B. Reddy and D. S. Juliet, “Transfer Learning with ResNet-50 for Malaria Cell-Image Classification,” in 2019 International Conference on Communication and Signal Processing (ICCSP), IEEE, Apr. 2019, pp. 0945–0949. https://doi.org/10.1109/ICCSP.2019.8697909
X. X. Li, D. Li, W. X. Ren, and J. S. Zhang, “Loosening Identification of Multi-Bolt Connections Based on Wavelet Transform and ResNet-50 Convolutional Neural Network,” Sensors, vol. 22, no. 18, Sep. 2022. https://doi.org/10.3390/s22186825
G. S. Nugraha, M. I. Darmawan, and R. Dwiyansaputra, “Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16, Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, May 2023. https://doi.org/10.22219/kinetik.v8i2.1667
M. Dhanaraju, P. Chenniappan, K. Ramalingam, S. Pazhanivelan, and R. Kaliaperumal, “Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture,” Agriculture (Switzerland), vol. 12, no. 10. MDPI, Oct. 01, 2022. https://doi.org/10.3390/agriculture12101745
V. Maeda-Gutiérrez et al., “Comparison of convolutional neural network architectures for classification of tomato plant diseases,” Applied Sciences (Switzerland), vol. 10, no. 4, Feb. 2020. https://doi.org/10.3390/app10041245
S. G. Paul et al., “A real-time application-based convolutional neural network approach for tomato leaf disease classification,” Array, vol. 19, Sep. 2023. https://doi.org/10.1016/j.array.2023.100313
H. C. Chen et al., “AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf,” Electronics (Switzerland), vol. 11, no. 6, Mar. 2022. https://doi.org/10.3390/electronics11060951
A. Sembiring, Y. Away, F. Arnia, and R. Muharar, “Development of Concise Convolutional Neural Network for Tomato Plant Disease Classification Based on Leaf Images,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Mar. 2021. https://doi.org/10.1088/1742-6596/1845/1/012009
Z. Azouz, B. Honarvar Shakibaei Asli, and M. Khan, “Evolution of Crack Analysis in Structures Using Image Processing Technique: A Review,” Electronics (Basel), vol. 12, no. 18, p. 3862, Sep. 2023. https://doi.org/10.3390/electronics12183862
M. M. I. Al-Ghiffary, C. A. Sari, E. H. Rachmawanto, N. M. Yacoob, N. R. D. Cahyo, and R. R. Ali, “Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture,” Advance Sustainable Science Engineering and Technology, vol. 5, no. 3, p. 0230304, Oct. 2023. https://doi.org/10.26877/asset.v5i3.17017
A. Deshpande, V. V. Estrela, and P. Patavardhan, “The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50,” Neuroscience Informatics, vol. 1, no. 4, p. 100013, Dec. 2021. https://doi.org/10.1016/j.neuri.2021.100013
S. Showkat and S. Qureshi, “Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia,” Chemometrics and Intelligent Laboratory Systems, vol. 224, May 2022. https://doi.org/10.1016/j.chemolab.2022.104534
Y. Zheng et al., “Application of transfer learning and ensemble learning in image-level classification for breast histopathology,” Intelligent Medicine, vol. 3, no. 2, pp. 115–128, May 2023. https://doi.org/10.1016/j.imed.2022.05.004
D. Agarwal, G. Marques, I. de la Torre-Díez, M. A. Franco Martin, B. García Zapiraín, and F. Martín Rodríguez, “Transfer learning for alzheimer’s disease through neuroimaging biomarkers: A systematic review,” Sensors, vol. 21, no. 21. MDPI, Nov. 01, 2021. https://doi.org/10.3390/s21217259
A. Theissler, M. Thomas, M. Burch, and F. Gerschner, “ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices,” Knowl Based Syst, vol. 247, Jul. 2022. https://doi.org/10.1016/j.knosys.2022.108651
S. R. Waheed, N. M. Suaib, M. S. Mohd Rahim, M. Mundher Adnan, and A. A. Salim, “Deep Learning Algorithms-based Object Detection and Localization Revisited,” in Journal of Physics: Conference Series, IOP Publishing Ltd, May 2021. https://doi.org/10.1088/1742-6596/1892/1/012001