Classification of Tea Leaf Diseases Based on ResNet-50 and Inception V3


  • Liana Trihardianingsih Universitas AMIKOM Yogyakarta, Indonesia
  • Andi Sunyoto Universitas AMIKOM Yogyakarta, Indonesia
  • Tonny Hidayat Universitas AMIKOM Yogyakarta, Indonesia




Classification, Tea Leaf Disease, Batch Size, Deep Learning, CNN, ResNet50


Technological advances have made a major contribution to controlling plant diseases. One method for resolving issues with plant disease identification is the use of deep learning for digital image processing. Tea leaf disease is a plant disease that requires fast and effective control. So, in this study, we adopted the Convolutional Neural Network (CNN) architectures, namely ResNet-50 and Inception V3, to classify six types of diseases that attack leaves. The amount of data used was 5867, which were divided into six classes, namely healthy leaf, algal spot, brown blight, gray blight, helopeltis, and red spot. The process of distributing the data involves randomly splitting it into three portions, with an allocation of 80% for training, 10% for validation, and 10% for testing. The process of classification is carried out by adjusting the use of batch sizes in the training process to maximizehyperparameters. The batch sizes used are 16, 32, and 64. Using three different batch size scenarios for each model, it shows that ResNet-50 has better performance on batch size 32 with an accuracy value of 97.44%, while Inception V3 has the best performance on batch size 64 with an accuracy of 97.62%..

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Author Biographies

Liana Trihardianingsih, Universitas AMIKOM Yogyakarta, Indonesia




Andi Sunyoto, Universitas AMIKOM Yogyakarta, Indonesia




Tonny Hidayat, Universitas AMIKOM Yogyakarta, Indonesia




Bao, W., Fan, T., Hu, G., Liang, D., & Li, H. (2022). Detection and identification of tea leaf diseases based on AX-RetinaNet. Scientific Reports, 12(1), 1–16.

Chen, J., Liu, Q., & Gao, L. (2019). Visual Tea Leaf Disease Recognition Using A Convolutional Neural Network Model. Symmetry, 11(3).

Datta, S., & Gupta, N. (2023). A Novel Approach For the Neural Detection of Tea Leaf Disease Using Deep Network Deep Datta Neural Network. Procedia Computer Science, 218, 2273–2286.

Faiz Nashrullah, Suryo Adhi Wibowo, & Gelar Budiman. (2020). The Investigation of Epoch Parameters in ResNet-50 Architecture for Pornographic Classification. Journal of Computer, Electronic, and Telecommunication, 1(1), 1–8.

Gayathri, S., Wise, D. C. J. W., Shamini, P. B., & Muthukumaran, N. (2020). Image Analysis and Detection of Tea Leaf Disease using Deep Learning. Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, Icesc, 398–403.

Hardi, N. (2022). Komparasi Algoritma MobileNet Dan Nasnet Mobile Pada Klasifikasi Penyakit Daun Teh. Reputasi: Jurnal Rekayasa Perangkat Lunak, 3(1), 50–55.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.

Hu, G., Wang, H., Zhang, Y., & Wan, M. (2021). Detection And Severity Analysis Of Tea Leaf Blight Based On Deep Learning. Computers and Electrical Engineering, 90(January 2020), 107023.

Kandel, I., & Castelli, M. (2020). The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express, 6(4), 312–315.

Latha, R. S., Sreekanth, G. R., Suganthe, R. C., Rajadevi, R., Karthikeyan, S., Kanivel, S., & Inbaraj, B. (2021). Automatic Detection of Tea Leaf Diseases using Deep Convolution Neural Network. 2021 International Conference on Computer Communication and Informatics, ICCCI 2021.

Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data Pre-Processing and Data Augmentation Techniques. Global Transitions Proceedings, 3(1), 91–99.

Mathew, M. P., & Mahesh, T. Y. (2022). Leaf-based disease detection in bell pepper plant using YOLO v5. Signal, Image and Video Processing, 16(3), 841–847.

Pandian, J. A., Nisha, S. N., Kanchanadevi, K., Pandey, A. K., & Rima, S. K. (2023). Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network. Computational Intelligence and Neuroscience, 2023, 1–11.

Ramdan, A., Heryana, A., Arisal, A., Kusumo, R. B. S., & Pardede, H. F. (2020). Transfer Learning and Fine-Tuning for Deep Learning-Based Tea Diseases Detection on Small Datasets. Proceeding - 2020 International Conference on Radar, Antenna, Microwave, Electronics and Telecommunications, ICRAMET 2020, 206–211.

Ramdan, A., Suryawati, E., Kusumo, R. B. S., Pardede, H. F., Mahendra, O., Dahlan, R., Fauziah, F., & Syahrian, H. (2019). Deep CNN Based Detection for Tea Clone Identification. Jurnal Elektronika Dan Telekomunikasi, 19(2), 45.

Rosyidah, E. A., Hadi, A. F., & Dewi, Y. S. (2023). The Classification of Tea Leaf Disease Using. Atlantis Press International BV.

Suttapakti, U., & Bunpeng, A. (2019). Potato Leaf Disease Classification Based on Distinct Color and Texture Feature Extraction. Proceedings - 2019 19th International Symposium on Communications and Information Technologies, ISCIT 2019, Mcd, 82–85.

Yuwana, R. S., Fauziah, F., Heryana, A., Krisnandi, D., Kusumo, R. B. S., & Pardede, H. F. (2020). Data Augmentation using Adversarial Networks for Tea Diseases Detection. Jurnal Elektronika Dan Telekomunikasi, 20(1), 29.

Zahisham, Z., Lee, C. P., & Lim, K. M. (2020). Food Recognition with ResNet-50. 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 1–5.


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How to Cite

Trihardianingsih, L. ., Sunyoto, A. ., & Tonny Hidayat. (2023). Classification of Tea Leaf Diseases Based on ResNet-50 and Inception V3. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1564-1573.