Plant Disease Detection by Using Mobilentv2 and Xception on Filtered and Enhanced Images
January 2, 2024
January 2, 2024
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The gathering, sorting, and processing of plant leaf images serves as the foundation for this study. These are crucial first steps in the plant health monitoring process that guarantee reliable findings. The work classifies and detects plant leaf photos, extracting data on plant health using state-of-the-art deep learning models like Xception and MobileNetV2. In order to assess the effectiveness of the system, additional filters are applied to the photos of plant leaves in order to adjust characteristics like brightness, contrast, sharpness, and blur. The study's results show that the deep learning models employed could accurately determine the health of plant leaves, offering important new information for related future research.
Yamaçli, V., & Yildirim, M. K. (2024). Plant Disease Detection by Using Mobilentv2 and Xception on Filtered and Enhanced Images. Engineering And Technology Journal, 9(01), 3266–3270. https://doi.org/10.47191/etj/v9i01.02
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2. Yamacli, V., Yildirim, K. M. 2023. Health Classification for Plants by Using Computer-Aided Deep Learning Methods in Proceedings of the International Conference on Advances and Innovations in Engineering.
3. Alharbi, A., Khan, M. U. G., and Tayyaba, B., “Wheat Disease Classification Using Continual Learning”, IEEE Access, 2023.
4. Vallabhajosyula, S., Sistla, V., and Kolli, V. K. K., “Transfer learning-based deep ensemble neural network for plant leaf disease detection”, Journal of Plant Diseases and Protection, 129, 545–558, 2022.
5. Hassan, S. M., and Maji, A. K., “Deep Feature-Based Plant Disease Identification Using Machine Learning Classifier”, Innovations in Systems and Software Engineering, 2023.
6. Hosny, K. M., El-Hady, W. M., Samy, F. M., Vrochidou, E., and Papakostas, G. A., “Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern”, IEEE Access, 2023.
7. Kaur, P., Harnal, S., Gautam, V., Singh, M. P., and Singh, S. P., “A Novel Transfer Deep Learning Method for Detection and Classification of Plant Leaf Disease”, Journal of Ambient Intelligence and Humanized Computing, 2022.
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10. Chollet, F., “Xception: Deep Learning with Depthwise Separable Convolutions”, Computer Vision and Pattern Recognition, arXiv:1610.02357, 2017.
11. Patil, M. A., and Manur, M., “Sensitive Crop Leaf Disease Prediction Based on Computer Vision Techniques with Handcrafted Feature”, International Journal of System Assurance Engineering and Management, 2023.
12. Ahmad, A., Gamal, A. E., and Saraswat, D., “Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions”, IEEE Access, 2023.
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15. https://pillow.readthedocs.io/en/stable/reference/ImageFilter.html/ [Accessed on December 2023].
16. https://pillow.readthedocs.io/en/stable/reference/ImageEnhance.html/ [Accessed on December 2023].
