Classification of Apple Tree Leaf Diseases using Pretrained EfficientNetB0 and XGBoost

Bagus Al Qohar, Ahmad Ubai Dullah, Aditya Yoga Darmawan, Jumanto Unjung

Abstract


The diseases that affect apple tree leaves seriously compromise agricultural production; therefore, early and accurate diagnosis is quite important for good disease control. Machine learning's recent developments have opened fascinating possibilities for automating the detection process and enhancing methods of precision agriculture. This study aims to create a strong classification model that can accurately and efficiently identify various diseases that affect apple tree leaves. The approach combines the pre-trained EfficientNetB0 architecture for feature extraction with the XGBoost model for classification, utilizing the advantages of both deep learning and gradient-boosting methods. With high performance measures including a macro-average precision of 95.86%, recall of 95.44%, and F1 score of 95.64%, the model achieved a classification accuracy of 95.74%. Furthermore, the average ROC-AUC score of 0.9964 emphasizes how well the model differentiates the five disease categories. This work stands out due to its hybrid approach, which integrates a robust pre-trained convolutional neural network (EfficientNetB0) with the XGBoost model. This significantly improves the accuracy of disease classification. This approach presents a novel pathway for precision agriculture, providing a reliable and effective instrument for the automatic identification of diseases in apple orchards.

Keywords


Apple Leaf Diseases, Classification Model, EfficientNetB0, Machine Learning, XGBoost

References


S. Bashir, F. Firdous, and S. Z. Rufai, “A Comprehensive Review on Apple Leaf Disease Detection,” in 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), IEEE, Apr. 2023, pp. 1–6. doi: 10.1109/I2CT57861.2023.10126487.

V. K. Vishnoi, K. Kumar, B. Kumar, S. Mohan, and A. A. Khan, “Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network,” IEEE Access, vol. 11, pp. 6594–6609, 2023, doi: 10.1109/ACCESS.2022.3232917.

S. Chakraborty, S. Paul, and M. Rahat-uz-Zaman, “Prediction of Apple Leaf Diseases Using Multiclass Support Vector Machine,” in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), IEEE, Jan. 2021, pp. 147–151. doi: 10.1109/ICREST51555.2021.9331132.

S. K, V. R. P, R. P, P. K. M, and P. S, “Apple Leaf Disease Detection using Deep Learning,” in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), IEEE, Mar. 2022, pp. 1063–1067. doi: 10.1109/ICCMC53470.2022.9753985.

Y. Zhong and M. Zhao, “Research on deep learning in apple leaf disease recognition,” Comput. Electron. Agric., vol. 168, p. 105146, Jan. 2020, doi: 10.1016/j.compag.2019.105146.

E. Kannan, C. M. B. M J, A. D. S, R. N. N, A. Begum, and H. D, “Deep Learning Techniques Advancements in Apple Leaf Disease Detection,” Procedia Comput. Sci., vol. 235, pp. 713–722, 2024, doi: 10.1016/j.procs.2024.04.068.

B. Liu, X. Huang, L. Sun, X. Wei, Z. Ji, and H. Zhang, “MCDCNet: Multi-scale constrained deformable convolution network for apple leaf disease detection,” Comput. Electron. Agric., vol. 222, p. 109028, Jul. 2024, doi: 10.1016/j.compag.2024.109028.

H. Wang, J. Zhang, Z. Yin, L. Huang, J. Wang, and X. Ma, “A deep evidence fusion framework for apple leaf disease classification,” Eng. Appl. Artif. Intell., vol. 136, p. 109011, Oct. 2024, doi: 10.1016/j.engappai.2024.109011.

B. U. Rani, K. Pavani, S. Bhavani, and G. Alapati, “A Systematic Analysis of Deep Learning and Machine Learning Methods for Identifying Apple Leaf Disease,” in 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, Jul. 2023, pp. 761–765. doi: 10.1109/ICESC57686.2023.10192948.

M. Sebastian, S. M S, and C. M. Antony, “Apple Leaf Disease Detection: Machine Learning & Deep Learning Techniques,” in 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS), IEEE, Dec. 2023, pp. 1–5. doi: 10.1109/ICCEBS58601.2023.10449037.

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, p. 47, Nov. 2023, doi: 10.24014/ijaidm.v7i1.24586.

S. Zhu, W. Ma, J. Lu, B. Ren, C. Wang, and J. Wang, “A novel approach for apple leaf disease image segmentation in complex scenes based on two-stage DeepLabv3+ with adaptive loss,” Comput. Electron. Agric., vol. 204, p. 107539, Jan. 2023, doi: 10.1016/j.compag.2022.107539.

C. Yan and K. Yang, “FSM-YOLO: Apple leaf disease detection network based on adaptive feature capture and spatial context awareness,” Digit. Signal Process., vol. 155, p. 104770, Dec. 2024, doi: 10.1016/j.dsp.2024.104770.

O. H. Kesav and R. G. K, “A Systematic Study on Enhanced Deep Learning Based Methodologies for Detection and Classification of Early Stage Cancers,” in 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE, Oct. 2023, pp. 328–333. doi: 10.1109/ICCCMLA58983.2023.10346973.

S. Qian, T. Peng, Z. Tao, X. Li, M. S. Nazir, and C. Zhang, “An evolutionary deep learning model based on XGBoost feature selection and Gaussian data augmentation for AQI prediction,” Process Saf. Environ. Prot., vol. 191, pp. 836–851, Nov. 2024, doi: 10.1016/j.psep.2024.08.119.

Kaggle, “Apple Tree Leaf Disease Dataset,” Kaggle. Accessed: Oct. 02, 2024. [Online]. Available: https://www.kaggle.com/datasets/nirmalsankalana/apple-tree-leaf-disease-dataset

N. R. Billa, B. P. Das, M. Biswal, and M. Okade, “CNN based image resizing forensics for double compressed JPEG images,” J. Inf. Secur. Appl., vol. 81, p. 103693, Mar. 2024, doi: 10.1016/j.jisa.2023.103693.

M. T R, M. Gupta, A. T A, V. Kumar V, O. Geman, and D. Kumar V, “An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging,” J. Neurosci. Methods, vol. 410, p. 110227, Oct. 2024, doi: 10.1016/j.jneumeth.2024.110227.

Bhoomika and G. Verma, “Enhancing Coffee Plant Disease Identification with EfficientNetB0 and Deep Learning,” in 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), IEEE, Oct. 2024, pp. 1423–1427. doi: 10.1109/I-SMAC61858.2024.10714676.

M. Niazkar et al., “Applications of XGBoost in water resources engineering: A systematic literature review (Dec 2018–May 2023),” Environ. Model. Softw., vol. 174, p. 105971, Mar. 2024, doi: 10.1016/j.envsoft.2024.105971.

K. M. K. Raghunath, V. V. Kumar, M. Venkatesan, K. K. Singh, T. R. Mahesh, and A. Singh, “XGBoost Regression Classifier (XRC) Model for Cyber Attack Detection and Classification Using Inception V4,” J. Web Eng., Apr. 2022, doi: 10.13052/jwe1540-9589.21413.

D. A. A. Pertiwi, K. Ahmad, S. N. Salahudin, A. M. Annegrat, and M. A. Muslim, “Using Genetic Algorithm Feature Selection to Optimize XGBoost Performance in Australian Credit,” J. Soft Comput. Explor., vol. 5, no. 1, pp. 92–98, Apr. 2024, doi: 10.52465/joscex.v5i1.302.

M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.

L.-E. Pomme, R. Bourqui, R. Giot, and D. Auber, “Relative Confusion Matrix: Efficient Comparison of Decision Models,” in 2022 26th International Conference Information Visualisation (IV), IEEE, Jul. 2022, pp. 98–103. doi: 10.1109/IV56949.2022.00025.




DOI: http://dx.doi.org/10.24014/coreit.v11i2.33174

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License  site stats  
Jurnal CoreIT by http://ejournal.uin-suska.ac.id/index.php/coreit/ is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.