COMPARATIVE STUDY ANALYSIS TOMATO LEAF DISEASES USING DEEP LEARNING MODELS CONVOLUTIONAL NEURAL NETWORK
DOI:
https://doi.org/10.33480/jitk.v12i1.8440Keywords:
Comparative Study, Deep Learning, EfficientNetB0, InceptionV3, MobileNetV2Abstract
Diseases that significantly impact tomato foliage contribute to decreased agricultural productivity and reduced crop quality, thus requiring accurate and efficient detection methods. This study aims to evaluate the effectiveness of three Convolutional Neural Network (CNN) architectures, namely EfficientNetB0, InceptionV3, and MobileNetV2, in classifying tomato leaf diseases using images. The dataset used in this study consists of 4,804 tomato leaf images sourced from field observations as well as publicly available datasets from Kaggle and Mendeley. In this dataset, five disease categories are recognized: Bacterial Spot (1,116 images), Early Blight (1,158 images), Late Blight (1,063 images), Septoria Leaf Spot (1,137 images), and Mosaic Virus (330 images). This study uses an experimental approach that includes image pre-processing, dataset splitting (70% for training and 30% for testing), transfer learning-based models, and performance evaluation using metrics such as accuracy, precision, memory, F1 score, and error matrix. The results show that EfficientNetB0 achieved the highest accuracy of 74.6%, followed by MobileNetV2 at 73.21% and InceptionV3 at 68.91%. Furthermore, MobileNetV2 demonstrated excellent computational efficiency, having the fewest parameters and the fastest inference time. Further Grad-CAM analysis confirmed that EfficientNetB0 primarily focused on disease-related tomato leaf regions during its prediction process. These findings indicate that EfficientNetB0 offers the best overall classification performance, while MobileNetV2 achieves a favorable balance between prediction accuracy and computational efficiency, making it suitable for resource-constrained agricultural applications.
Downloads
References
[1] Md. R. Islam, Md. A. M. T. Siddique, M. Amiruzzaman, M. Abdullah-Al-Wadud, S. M. R. A. Masud, and A. K. Saha, “An Efficient Technique for Recognizing Tomato Leaf Disease Based on the Most Effective Deep CNN Hyperparameters,” AETiC, vol. 7, no. 1, pp. 1–14, Jan. 2023, doi: 10.33166/AETiC.2023.01.001.
[2] N. Khasawneh, E. Faouri, and M. Fraiwan, “Automatic Detection of Tomato Diseases Using Deep Transfer Learning,” Applied Sciences, vol. 12, no. 17, p. 8467, Aug. 2022, doi: 10.3390/app12178467.
[3] Z. Ullah, N. Alsubaie, M. Jamjoom, S. H. Alajmani, and F. Saleem, “EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images,” Agriculture, vol. 13, no. 3, p. 737, Mar. 2023, doi: 10.3390/agriculture13030737.
[4] M. Shoaib, B. Shah, N. Sayed, F. Ali, R. Ullah, and I. Hussain, “Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection,” Front. Plant Sci., vol. 14, Oct. 2023, doi: 10.3389/fpls.2023.1283235.
[5] Y. Zhong, Z. Teng, and M. Tong, “LightMixer: A novel lightweight convolutional neural network for tomato disease detection,” Front. Plant Sci., vol. 14, May 2023, doi: 10.3389/fpls.2023.1166296.
[6] P. Palupiningsih, A. R. Sujiwanto, and R. R. B. P. Prawirodirjo, “Analisis Perbandingan Performa Model Klasifikasi Kesehatan Daun Tomat menggunakan arsitektur VGG, MobileNet, dan Inception V3,” JIKA, vol. 10, no. 1, pp. 98–110, Jun. 2023, doi: 10.29244/jika.10.1.98-110.
[7] Shivraj Rahane, Shubham Rahane, Bhushan Patole, Tejaswini Shelke, Dr. Sunil Khatal, and Prof. Mundhe B. B., “Classification of Tomato Plant Diseases using Deep Learning Based Classifier from Images of Leaves,” IJARSCT, pp. 278–284, Nov. 2022, doi: 10.48175/IJARSCT-7635.
[8] M. Bhandari, T. B. Shahi, A. Neupane, and K. B. Walsh, “BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model,” Journal of Imaging, vol. 9, no. 2, p. 53, Feb. 2023, doi: 10.3390/jimaging9020053.
[9] A. Upadhyay et al., “Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture,” Artif Intell Rev, vol. 58, no. 3, p. 92, Jan. 2025, doi: 10.1007/s10462-024-11100-x.
[10] A. Mimi et al., “Identifying selected diseases of leaves using deep learning and transfer learning models,” Machine Graphics & Vision, vol. 32, no. 1, pp. 55–71, Apr. 2023, doi: 10.22630/MGV.2023.32.1.3.
[11] H. Y. Bayram, H. Bingol, and B. Alatas, “Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases,” TS, vol. 39, no. 5, pp. 1781–1787, Nov. 2022, doi: 10.18280/ts.390537.
[12] H. Ulutaş and V. Aslantaş, “Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model,” Electronics, vol. 12, no. 4, p. 827, Jan. 2023, doi: 10.3390/electronics12040827.
[13] Ramanjot et al., “Plant Disease Detection and Classification: A Systematic Literature Review,” Sensors, vol. 23, no. 10, p. 4769, May 2023, doi: 10.3390/s23104769.
[14] Md. M. Rana, T. A. Tithy, N. R. Mamun, and H. K. Sharker, “Plant Leaf Diseases Identification in Deep Learning,” CSEIJ, vol. 12, no. 5, pp. 1–13, Oct. 2022, doi: 10.5121/cseij.2022.12501.
[15] S. G. Paul et al., “A real-time application-based convolutional neural network approach for tomato leaf disease classification,” Array, vol. 19, p. 100313, Sep. 2023, doi: 10.1016/j.array.2023.100313.
[16] Romi Antoni et al., “Klasifikasi Penyakit Daun Tomat Menggunakan Pengolahan Citra Dan Algoritma Machine Learning,” JUKTISI, vol. 4, no. 3, pp. 1933–1942, Jan. 2026, doi: 10.62712/juktisi.v4i3.778.
[17] O. Attallah, “Tomato Leaf Disease Classification via Compact Convolutional Neural Networks with Transfer Learning and Feature Selection,” Horticulturae, vol. 9, no. 2, p. 149, Jan. 2023, doi: 10.3390/horticulturae9020149.
[18] N. Kundu et al., “Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning,” Artificial Intelligence in Agriculture, vol. 6, pp. 276–291, 2022, doi: 10.1016/j.aiia.2022.11.002.
[19] M. Nawaz et al., “A robust deep learning approach for tomato plant leaf disease localization and classification,” Sci Rep, vol. 12, no. 1, p. 18568, Nov. 2022, doi: 10.1038/s41598-022-21498-5.
[20] S. A. Wagle, H. R, V. Varadarajan, and K. Kotecha, “A New Compact Method Based on a Convolutional Neural Network for Classification and Validation of Tomato Plant Disease,” Electronics, vol. 11, no. 19, p. 2994, Sep. 2022, doi: 10.3390/electronics11192994.
[21] A. Saeed, A. A. Abdel-Aziz, A. Mossad, M. A. Abdelhamid, A. Y. Alkhaled, and M. Mayhoub, “Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks,” Agriculture, vol. 13, no. 1, p. 139, Jan. 2023, doi: 10.3390/agriculture13010139.
[22] Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India, B. Balaji, T. S. Murthy, and R. Kuchipudi, “A Comparative Study on Plant Disease Detection and Classification Using Deep Learning Approaches,” IJIGSP, vol. 15, no. 3, pp. 48–59, Jun. 2023, doi: 10.5815/ijigsp.2023.03.04.
[23] H. T. Nguyen, H. H. Luong, L. B. Huynh, B. Q. H. Le, N. H. Doan, and D. T. D. Le, “An Improved MobileNet for Disease Detection on Tomato Leaves,” Advances in Technology Innovation, vol. 8, no. 3, pp. 192–209, Jul. 2023, doi: 10.46604/aiti.2023.11568.
[24] S. Jadhav and A. M. Lal, “Multi-Class Plant Leaf Disease Detection Using a Deep Convolutional Neural Network,” International Journal of Information System Modeling and Design (IJISMD), vol. 13, no. 1, pp. 1–14, 2022, doi: 10.4018/IJISMD.315126.
[25] A. Bastian, A. I. Nurzaman, T. F. Prasetyo, and S. Fatimah, “Roselle Pest Detection and Classification Using Threshold and Template Matching,” JOIG, vol. 11, no. 4, pp. 330–342, Dec. 2023, doi: 10.18178/joig.11.4.330-342.
[26] J. Zheng and M. Du, “Study on Tomato Disease Classification based on Leaf Image Recognition based on Deep Learning Technology,” International Journal of Advanced Computer Science and Applications, vol. 14, Apr. 2023, doi: 10.14569/IJACSA.2023.0140463.
[27] M. S. Alzahrani and F. W. Alsaade, “Transform and Deep Learning Algorithms for the Early Detection and Recognition of Tomato Leaf Disease,” Agronomy, vol. 13, no. 5, p. 1184, May 2023, doi: 10.3390/agronomy13051184.
[28] T. M. Arkan, A. Sugiharto, and H. A. Wibawa, “Optimizing VGG16 Architecture with Bayesian Hyperparameter Tuning for Tomato Leaf Disease Classification,” Jurnal Masyarakat Informatika, vol. 16, no. 2, pp. 162–173, Jun. 2025, doi: 10.14710/jmasif.16.2.73168.
[29] J. Liang and W. Jiang, “A ResNet50-DPA model for tomato leaf disease identification,” Front. Plant Sci., vol. 14, p. 1258658, Oct. 2023, doi: 10.3389/fpls.2023.1258658.
[30] A. Anton, S. Rustad, G. F. Shidik, and A. Syukur, “COMPARATIVE ANALYSIS OF CNN ARCHITECTURES FOR TOMATO LEAF DISEASE CLASSIFICATION USING TRANSFER LEARNING,” jitk, vol. 11, no. 1, pp. 125–135, Aug. 2025, doi: 10.33480/jitk.v11i1.7014.
[31] T. F. Prasetyo, Sunardi, and A. Fadlil, “Model Prediktif Penyakit Tanaman Tomat Menggunakan Arsitektur EfficientNetB0,” TEMATIK, vol. 12, no. 2, pp. 212–222, Jan. 2026, doi: 10.38204/tematik.v12i2.2722.
[32] S. Thuseethan, P. Vigneshwaran, J. Charles, and C. Wimalasooriya, “Siamese Network-Based Lightweight Framework for Tomato Leaf Disease Recognition,” Computers, vol. 13, no. 12, p. 323, Dec. 2024, doi: 10.3390/computers13120323.
[33] Y. Kaya and E. Gürsoy, “A review of deep learning architectures for plant disease detection,” Turkish Journal of Biology, vol. 49, no. 5, pp. 459–497, Oct. 2025, doi: 10.55730/1300-0152.2761.
[34] S. Kapoor et al., “REFORMS: Consensus-based Recommendations for Machine-learning-based Science,” Sci. Adv., vol. 10, no. 18, p. eadk3452, May 2024, doi: 10.1126/sciadv.adk3452.
[35] M. Xu, S. Yoon, Y. Jeong, and D. S. Park, “Transfer learning for versatile plant disease recognition with limited data,” Front. Plant Sci., vol. 13, Nov. 2022, doi: 10.3389/fpls.2022.1010981.
[36] K. Alomar, H. I. Aysel, and X. Cai, “Data Augmentation in Classification and Segmentation: A Survey and New Strategies,” Journal of Imaging, vol. 9, no. 2, p. 46, Feb. 2023, doi: 10.3390/jimaging9020046.
[37] D. Himaja, D. Venkatesulu, and U. Srilakshmi, “Analyzing Adaptive and Non-Adaptive Online Learners on Imbalanced Evolving Streams,” Informatica, vol. 47, no. 5, May 2023, doi: 10.31449/inf.v47i5.4527.
[38] P. Alirezazadeh, M. Schirrmann, and F. Stolzenburg, “Improving Deep Learning-based Plant Disease Classification with Attention Mechanism,” Gesunde Pflanzen, vol. 75, no. 1, pp. 49–59, Feb. 2023, doi: 10.1007/s10343-022-00796-y.
[39] T. T. Mac, T.-D. Nguyen, H.-K. Dang, D.-T. Nguyen, and X.-T. Nguyen, “Intelligent agricultural robotic detection system for greenhouse tomato leaf diseases using soft computing techniques and deep learning,” Sci Rep, vol. 14, no. 1, p. 23887, Oct. 2024, doi: 10.1038/s41598-024-75285-5.
[40] J. Zhang, X. Yu, X. Lei, and C. Wu, “A novel CapsNet neural network based on MobileNetV2 structure for robot image classification,” Front. Neurorobot., vol. 16, p. 1007939, Sep. 2022, doi: 10.3389/fnbot.2022.1007939.
[41] F. Shahoveisi, H. Taheri Gorji, S. Shahabi, S. Hosseinirad, S. Markell, and F. Vasefi, “Application of image processing and transfer learning for the detection of rust disease,” Sci Rep, vol. 13, p. 5133, Mar. 2023, doi: 10.1038/s41598-023-31942-9.
[42] U. Mishra, A. Pandey, L. G, and T. K, “Deep learning-based disease detection in potato and mango leaves: a comparative study of CNN, AlexNet, ResNet, and EfficientNet,” Sci Rep, vol. 16, no. 1, p. 2788, Dec. 2025, doi: 10.1038/s41598-025-32607-5.
[43] A. Chaddad, J. Peng, J. Xu, and A. Bouridane, “Survey of Explainable AI Techniques in Healthcare,” Sensors, vol. 23, no. 2, p. 634, Jan. 2023, doi: 10.3390/s23020634.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Desfi Silvia Aros, Tri Ferga Prasetyo, Harun Sujadi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
