Grape leaf disease classification using efficientnet feature extraction and catboostclassifier
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Abstract
Grapes are one of the most extensively cultivated crops worldwide due to their significant economic importance. However, the productivity of grape crops is often threatened by diseases caused by bacterial, fungal, or viral infections. Traditionally, the detection of infected grape leaves has been conducted through manual visual inspections, a method that is both time-consuming and prone to biases. Recent studies have leveraged transfer learning models to classify grape leaf diseases with high accuracy. Despite this progress, there is a notable gap in research exploring the integration of transfer learning for feature extraction and machine learning for feature classification in detecting grape leaf diseases. This study introduces a novel approach that combines transfer learning using EfficientNetB0 for feature extraction with a machine learning model, specifically Categorical Boosting (CatBoost), for feature classification. The proposed model demonstrates outstanding performance, achieving an accuracy of 99.56% on the test dataset, surpassing traditional transfer learning methods reported in previous studies.
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References
G. R. Caponio, F. Minervini, G. Tamma, G. Gambacorta, and M. De Angelis, “Promising Application of Grape Pomace and Its Agri-Food Valorization: Source of Bioactive Molecules with Beneficial Effects,” Sustain., vol. 15, no. 11, 2023, doi: 10.3390/su15119075.
P. Kandylis, D. Dimitrellou, and T. Moschakis, “Recent applications of grapes and their derivatives in dairy products,” Trends Food Sci. Technol., vol. 114, no. June 2020, pp. 696–711, 2021, doi: 10.1016/j.tifs.2021.05.029.
A. Atak, Z. Göksel, and Y. Yılmaz, “Changes in major phenolic compounds of seeds, skins, and pulps from various vitis spp. And the effect of powdery and downy mildew diseases on their levels in grape leaves,” Plants, vol. 10, no. 12, 2021, doi: 10.3390/plants10122554.
L. Capriotti, E. Baraldi, B. Mezzetti, C. Limera, and S. Sabbadini, “Biotechnological approaches: Gene overexpression, gene silencing, and genome editing to control fungal and oomycete diseases in grapevine,” Int. J. Mol. Sci., vol. 21, no. 16, pp. 1–29, 2020, doi: 10.3390/ijms21165701.
J. Zhu, M. Cheng, Q. Wang, H. Yuan, and Z. Cai, “Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning,” Front. Plant Sci., vol. 12, no. June, pp. 1–16, 2021, doi: 10.3389/fpls.2021.695749.
Z. Gao, L. R. Khot, R. A. Naidu, and Q. Zhang, “Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging,” Comput. Electron. Agric., vol. 179, no. July, p. 105807, 2020, doi: 10.1016/j.compag.2020.105807.
N. Kaur and V. Devendran, “A novel framework for semi-automated system for grape leaf disease detection,” Multimed. Tools Appl., vol. 83, no. 17, pp. 50733–50755, 2024, doi: 10.1007/s11042-023-17629-3.
M. A. Hasan, Y. Riyanto, and D. Riana, “Grape leaf image disease classification using CNN-VGG16 model,” J. Teknol. dan Sist. Komput., vol. 9, no. 4, pp. 218–223, 2021, doi: 10.14710/jtsiskom.2021.14013.
S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front. Plant Sci., vol. 7, no. September, pp. 1–10, 2016, doi: 10.3389/fpls.2016.01419.
B. Liu, Z. Ding, L. Tian, D. He, S. Li, and H. Wang, “Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks,” Front. Plant Sci., vol. 11, no. July, pp. 1–14, 2020, doi: 10.3389/fpls.2020.01082.
K. Z. Thet, K. K. Htwe, and M. M. Thein, “Grape Leaf Diseases Classification using Convolutional Neural Network,” Proc. 4th Int. Conf. Adv. Inf. Technol. ICAIT 2020, pp. 147–152, 2020, doi: 10.1109/ICAIT51105.2020.9261801.
A. K. Uttam, “Grape Leaf Disease Prediction Using Deep Learning,” in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2022, pp. 369–373. doi: 10.1109/ICAAIC53929.2022.9792739.
P. Kaur, S. Harnal, R. Tiwari, S. Upadhyay, S. Bhatia, and A. Mashat, “Recognition of Leaf Disease Using Hybrid Convolutional,” Sensors, vol. 22, 2022.
A. K. Dutta and A. R. Wahab Sait, “A Fine-Tuned CatBoost-Based Speech Disorder Detection Model,” J. Disabil. Res., vol. 3, no. 3, pp. 1–8, 2024, doi: 10.57197/jdr-2024-0027.
A. R. W. Sait and A. M. A. B. Awad, “Ensemble Learning-Based Coronary Artery Disease Detection Using Computer Tomography Images,” Appl. Sci., vol. 14, no. 3, 2024, doi: 10.3390/app14031238.
R. Mandal, “Grapevine Disease Dataset (Original),” Kaggle, 2023. https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original
J. S. Hwang, S. S. Lee, J. W. Gil, and C. K. Lee, “Determination of Optimal Batch Size of Deep Learning Models with Time Series Data,” Sustain., vol. 16, no. 14, pp. 1–11, 2024, doi: 10.3390/su16145936.
Y. Wu and J. Johnson, “Rethinking ‘Batch’ in BatchNorm,” 2021, [Online]. Available: http://arxiv.org/abs/2105.07576
Y. Kumar, J. Kumar, and P. Sheoran, “Integration of cloud computing in BCI: A review,” Biomed. Signal Process. Control, vol. 87, no. PA, p. 105548, 2024, doi: 10.1016/j.bspc.2023.105548.
H. Syamsudin, S. Khalidah, and J. Unjung, “Lepidoptera Classification Using Convolutional Neural Network EfficientNet-B0,” Indones. J. Artif. Intell. Data Min., vol. 7, no. 1, pp. 47–56, 2023, doi: 10.24014/ijaidm.v7i1.24586.
S. Nigam, R. Jain, V. K. Singh, S. Marwaha, A. Arora, and S. Jain, “EfficientNet architecture and attention mechanism-based wheat disease identification model,” Procedia Comput. Sci., vol. 235, pp. 383–393, 2024, doi: 10.1016/j.procs.2024.04.038.
T. Selim, I. Elkabani, and M. A. Abdou, “Students Engagement Level Detection in Online e-Learning Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM,” IEEE Access, vol. 10, pp. 99573–99583, 2022, doi: 10.1109/ACCESS.2022.3206779.
X. An et al., “Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion,” Front. Neurosci., vol. 16, no. February, pp. 1–9, 2022, doi: 10.3389/fnins.2022.813056.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-Excitation Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 8, pp. 2011–2023, 2020, doi: 10.1109/TPAMI.2019.2913372.
L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “Catboost: Unbiased boosting with categorical features,” Adv. Neural Inf. Process. Syst., vol. 2018-December, no. Section 4, pp. 6638–6648, 2018.
H. Qiu, Y. Xia, C. Xiang, F. Xu, L. Sun, and Y. Zou, “Prediction of hydrogen storage in metal-organic frameworks using CatBoost-based approach,” Int. J. Hydrogen Energy, vol. 79, no. July, pp. 952–961, 2024, doi: 10.1016/j.ijhydene.2024.07.078.