COMPARATIVE ANALYSIS OF CNN ARCHITECTURES FOR TOMATO LEAF DISEASE CLASSIFICATION USING TRANSFER LEARNING
DOI:
https://doi.org/10.33480/jitk.v11i1.7014Keywords:
CNN , DenseNet121 , MobileNetV2 , Tomato Leaf Disease , XceptionAbstract
Tomato is one of the widely available horticultural products and holds significant economic value in Indonesia. However, its productivity is often disrupted by various leaf diseases. This study aims to compare the performance of three CNN architectures—DenseNet121, Xception, and MobileNetV2—in classifying tomato leaf diseases. The dataset used consists of 10,000 balanced images across ten classes: Bacterial Spot, Septoria Leaf Spot, Early Blight, Late Blight, Mosaic Virus, Yellow Leaf Curl Virus, Leaf Mold, Target Spot, Spider Mites Two-Spotted Spider Mite, and Healthy. All images were resized to 224x224 pixels and divided into 80% training data and 20% test data. Augmentation techniques were applied to balance the data across classes. Experimental results show that the Xception architecture outperforms the other models, achieving an accuracy of 98.79%, with a precision of 98.80%, recall of 98.79%, and an F1-Score of 98.78%. These findings indicate that the Xception model is highly effective for plant disease classification and is suitable for implementation in environments with limited resources.
Downloads
References
Inayah Rahman, Andini Cahyaning Pratiwi, and Putri Rizka Citaningati, “The Role Of The Agricultural Sector In Economic Growth In The Asean Region,” Jurnal Ekonomi Pertanian dan Agribisnis (JEPA), vol. 8, no. 1, 2024.
BPS-RI, Statistik Hortikultura 2020, vol. 2020, no. 30. 2020. doi: 10.21608/jkom.2020.120332.
N. Khasawneh, E. Faouri, and M. Fraiwan, “Automatic Detection of Tomato Diseases Using Deep Transfer Learning,” Applied Sciences, 2022, doi: 10.3390/app12178467.
Wahyu Nurwijayo, “Penyakit Tanaman Tomat: Jenis, Penyebab, Gejala, dan Cara Penanggulangan,” https://gdm.id/penyakit-tanaman-tomat/. Accessed: Aug. 15, 2025. [Online]. Available: https://gdm.id/penyakit-tanaman-tomat/
M. Dutta et al., “Monitoring Root and Shoot Characteristics for the Sustainable Growth of Barley Using an IoT-Enabled Hydroponic System and AquaCrop Simulator,” Sustainability, vol. 15, no. 5, 2023, doi: 10.3390/su15054396.
D. L. Shanthi, K. Vinutha, N. Ashwini, and S. Vashistha, “Tomato Leaf Disease Detection Using CNN,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 2975–2984. doi: 10.1016/j.procs.2024.04.281.
A. Jasani, M. Dholi, and S. Purkar, “Tomato Leaf Disease Detection,” Int J Res Appl Sci Eng Technol, vol. 10, no. 5, pp. 918–922, May 2022, doi: 10.22214/ijraset.2022.41918.
P. Agarwal, S. Saha, and P. Hariprasad, “Agro-Industrial-Residues as Potting Media: Physicochemical and Biological Characters and Their Influence on Plant Growth,” Biomass Convers Biorefin, 2021, doi: 10.1007/s13399-021-01998-6.
S. Sagar, M. Javed, and D. S. Doermann, “Leaf-Based Plant Disease Detection and Explainable AI,” Dec. 2023, [Online]. Available: http://arxiv.org/abs/2404.16833
Md. J. A. Soeb et al., “Tea Leaf Disease Detection and Identification Based on YOLOv7 (YOLO-T),” Sci Rep, vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-33270-4.
S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach,” Electronics (Basel), 2021, doi: 10.3390/electronics10121388.
A. Rajbongshi, T. Khan, Md. M. Rahman, A. Pramanik, S. Md. Tanvir Siddiquee, and N. R. Chakraborty, “Recognition of Mango Leaf Disease Using Convolutional Neural Network Models: A Transfer Learning Approach,” Indonesian Journal of Electrical Engineering and Computer Science, 2021, doi: 10.11591/ijeecs.v23.i3.pp1681-1688.
A. S. A. N. D. E. Z. A. N. D. R. S. A. N. D. D. T. A. N. D. M. Fossa, “Smart Sensors and Smart Data for Precision Agriculture: A Review,” Jul. 2024, Multidisciplinary Digital Publishing Institute. doi: 10.3390/s24082647.
G. S. Hukkeri, B. C. Soundarya, H. L. Gururaj, and V. Ravi, “Classification of Various Plant Leaf Disease Using Pretrained Convolutional Neural Network On Imagenet,” Open Agric J, vol. 18, no. 1, May 2024, doi: 10.2174/0118743315305194240408034912.
S. Bensaadi and A. Louchene, “Low-Cost Convolutional Neural Network for Tomato Plant Diseases Classifiation,” Iaes International Journal of Artificial Intelligence (Ij-Ai), vol. 12, no. 1, p. 162, 2023, doi: 10.11591/ijai.v12.i1.pp162-170.
M. Altalak, M. A. Uddin, A. Alajmi, and A. Rizg, “A Hybrid Approach for the Detection and Classification of Tomato Leaf Diseases,” Applied Sciences (Switzerland), vol. 12, no. 16, Aug. 2022, doi: 10.3390/app12168182.
C. Sarkar, D. Gupta, U. Gupta, and B. B. Hazarika, “Leaf disease detection using machine learning and deep learning: Review and challenges,” Sep. 01, 2023, Elsevier Ltd. doi: 10.1016/j.asoc.2023.110534.
Y. Liu, G. Gao, and Z. Zhang, “Plant disease detection based on lightweight CNN model,” in 2021 4th International Conference on Information and Computer Technologies (ICICT), 2021, pp. 64–68. doi: 10.1109/ICICT52872.2021.00018.
G. Sakkarvarthi, G. W. Sathianesan, V. S. Murugan, A. J. Reddy, P. Jayagopal, and M. Elsisi, “Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network,” Electronics (Basel), vol. 11, no. 21, 2022, doi: 10.3390/electronics11213618.
S. A. Wagle, R. Harikrishnan, S. H. Md Ali, and M. Faseehuddin, “Classification of Plant Leaves Using New Compact Convolutional Neural Network Models,” Plants, 2021, doi: 10.3390/plants11010024.
H. Chen et al., “AlexNet Convolutional Neural Network for Disease Detection and Classification of Tomato Leaf,” Electronics (Basel), vol. 11, no. 6, p. 951, 2022, doi: 10.3390/electronics11060951.
O. Elsherbiny, A. Elaraby, M. D. Alahmadi, M. Hamdan, and J. Gao, “Rapid Grapevine Health Diagnosis Based on Digital Imaging and Deep Learning,” Plants, 2024, doi: 10.3390/plants13010135.
M. Bakr, S. AbdelGaber, M. Nasr, and M. Hazman, “Tomato Disease Detection Model Based on Densenet and Transfer Learning,” Applied Computer Science, vol. 18, no. 2, pp. 56–70, 2022, doi: 10.35784/acs-2022-13.
H. Amin, A. Darwish, A. E. Hassanien, and M. Soliman, “End-to-End Deep Learning Model for Corn Leaf Disease Classification,” IEEE Access, vol. 10, pp. 31103–31115, 2022, doi: 10.1109/ACCESS.2022.3159678.
N. Awalia and A. Primajaya, “Identifikasi Penyakit Leaf Mold Daun Tomat Menggunakan Model Densenet121 Berbasis Transfer Learning,” vol. 8, no. 1, 2022, [Online]. Available: http://ejournal.fikom-unasman.ac.id
S. C. Huang and T. H. Le, Principles and Labs for Deep Learning. Elsevier, 2021. doi: 10.1016/B978-0-323-90198-7.09993-6.
A. Mimi et al., “Identifying Selected Diseases of Leaves using Deep Learning and Transfer Learning Models,” Machine Graphics and Vision, vol. 32, no. 1, pp. 55–71, 2023, doi: 10.22630/MGV.2023.32.1.3.
G. Mukherjee, A. Chatterjee, and B. Tudu, “Identification of the types of disease for tomato plants using a modified gray wolf optimization optimized MobileNetV2 convolutional neural network architecture driven computer vision framework,” Concurr Comput, vol. 34, no. 22, p. e7161, 2022, doi: https://doi.org/10.1002/cpe.7161.
Z. Chen, X. Zhang, T. Lv, and G. Wang, “Using a Hybrid Convolutional Neural Network with a Transformer Model for Tomato Leaf Disease Detection,” Agronomy, vol. 14, no. 4, 2024.
H. ÇETİNER, “CLASSIFICATION OF APPLE LEAF DISEASES USING THE PROPOSED CONVOLUTION NEURAL NETWORK APPROACH,” Mühendislik Bilimleri ve Tasarım Dergisi, vol. 9, no. 4, pp. 1130–1140, Dec. 2021, doi: 10.21923/jesd.980629.
V. Gautam et al., “A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment,” Sustainability, vol. 14, no. 20, p. 13610, 2022, doi: 10.3390/su142013610.
S. Yang, W. Xiao, M. Zhang, S. Guo, J. Zhao, and F. Shen, “Image Data Augmentation for Deep Learning: A Survey,” Apr. 2022, [Online]. Available: http://arxiv.org/abs/2204.08610
M. V. Shewale and R. D. Daruwala, “High performance deep learning architecture for early detection and classification of plant leaf disease,” J Agric Food Res, vol. 14, Dec. 2023, doi: 10.1016/j.jafr.2023.100675.
Tejaswini, P. Rastogi, S. Dua, Manikanta, and V. Dagar, “Early Disease Detection in Plants using CNN,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 3468–3478. doi: 10.1016/j.procs.2024.04.327.
M. M. Mashamba, A. Telukdarie, I. Munien, U. Onkonkwo, and A. Vermeulen, “Detection of bacterial spot disease on tomato leaves using a Convolutional Neural Network (CNN),” in Procedia Computer Science, Elsevier B.V., 2024, pp. 602–609. doi: 10.1016/j.procs.2024.05.145.
T. Sanida, A. Sideris, M. V. Sanida, and M. Dasygenis, “Tomato leaf disease identification via two–stage transfer learning approach,” Smart Agricultural Technology, vol. 5, Oct. 2023, doi: 10.1016/j.atech.2023.100275.
S. M. Kiran and D. N. Chandrappa, “Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms,” Journal of Robotics and Control (JRC), vol. 4, no. 6, pp. 840–848, 2023, doi: 10.18196/jrc.v4i6.20342.
M. Gogoi, V. Kumar, S. A. Begum, N. Sharma, and S. Kant, “Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture With Transfer Learning Approach,” Agriculture, 2023, doi: 10.3390/agriculture13081505.
Zachary R. Murphy, Kesavan Venkatesh, Jeremias Sulam, and Paul H. Yi, “Visual Transformers and Convolutional Neural Networks for Disease Classification on Radiographs: A Comparison of Performance, Sample Efficiency, and Hidden Stratification,” Radiol Artif Intell, 2022.
V.-S. Doan, T. Huynh-The, and D.-S. Kim, “Underwater Acoustic Target Classification Based on Dense Convolutional Neural Network,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, doi: 10.1109/LGRS.2020.3029584.
S. Anwar, S. R. Soomro, S. K. Baloch, A. A. Patoli, and A. R. Kolachi, “Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases,” Engineering Technology & Applied Science Research, 2023, doi: 10.48084/etasr.6187.
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.
H. Ulutaş and V. Aslantaş, “Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model,” Electronics (Basel), vol. 12, no. 4, p. 827, 2023, doi: 10.3390/electronics12040827.
R.-Y. Ju, T.-Y. Lin, J.-H. Jian, and J.-S. Chiang, “Efficient convolutional neural networks on Raspberry Pi for image classification,” J Real Time Image Process, vol. 20, no. 2, p. 21, 2023, doi: 10.1007/s11554-023-01271-1.
H. A. Santoso, B. F. Safsalta, N. Febrianto, G. W. Saraswati, and S.-C. Haw, “Comparative Analysis of Convolutional Neural Network and DenseNet121 Transfer Learning in Agriculture Focusing on Crop Leaf Disease Identification,” Applied Computing and Informatics, 2024, doi: 10.1108/aci-03-2024-0132.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anton Anton, Supriadi Rustad, Guruh Fajar Shidik, Abdul Syukur
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
