OPTIMIZATION OF THE INCEPTIONV3 ARCHITECTURE FOR POTATO LEAF DISEASE CLASSIFICATION
DOI:
https://doi.org/10.33480/jitk.v10i4.6554Keywords:
cnn classification, fine-tuning pre-trained model, inceptionV3 optimization, plant disease detection, potato leaf diseaseAbstract
Potato leaf diseases can cause significant yield losses, making early detection crucial to prevent major damages. This study aims to optimize the Inception V3 architecture in a Convolutional Neural Network (CNN) for potato leaf disease classification by applying Fine Tuning Pre-Trained. This method leverages weights from a pre-trained model on a large-scale dataset, enhancing accuracy while reducing the risk of overfitting. The training process involves adjusting several final layers of Inception V3 to better adapt to specific features of potato leaf diseases. The results show that this approach improves classification performance, achieving an accuracy of 97.78%, precision of 98%, recall of 98%, and an F1-score of 98%. With better computational efficiency compared to previous architectures, this model is expected to be widely applicable in plant disease detection systems, particularly for farmers or institutions with limited resources.
Downloads
References
B. Gülmez, “A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture,” Potato Res., no. 0123456789, 2024, doi: 10.1007/s11540-024-09786-1.
S. N et al., “Revolutionizing Potato Crop Management: Deep learning-Driven Potato Disease Detection with Convolutional Neural Networks,” Int. J. Adv. Biochem. Res., vol. 8, no. 3, pp. 644–653, 2024, doi: 10.33545/26174693.2024.v8.i3h.811.
J. Palaskar, G. Gorde, C. More, S. Memane, and V. Gaikwad, “Potato Leaf Disease Detection Using Machine Learning,” Curr. Agric. Res. J., vol. 11, no. 3, pp. 949–954, 2023, doi: https://dx.doi.org/10.12944/CARJ.11.3.23.
A. Fauzi, A. E. Chandra, S. Imammah, M. Zapata, M. I. Marzuki, and S. Prayogi, “Machine Learning-Potato Leaf Disease Detection App ( MR-PoLoD ),” SISFOKOM (Sistem Inf. dan Komputer), vol. 13, no. September 2023, pp. 410–415, 2024, doi: 10.32736/sisfokom.v13i3.2261.
A. Kaur et al., “Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review,” IEEE Access, vol. 12, no. November, pp. 193902–193922, 2024, doi: 10.1109/ACCESS.2024.3510456.
H. Ghosh, I. S. Rahat, K. Shaik, S. Khasim, and M. Yesubabu, “Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks,” EAI Endorsed Trans. Scalable Inf. Syst., vol. 10, no. 6, pp. 1–8, 2023, doi: 10.4108/eetsis.3937.
T. A. Asfaw, “Deep learning hyperparameter’s impact on potato disease detection,” Sci. Temper, vol. 14, no. 03, pp. 582–590, 2023, doi: 10.58414/scientifictemper.2023.14.3.04.
A. Dolatabadian, T. X. Neik, M. F. Danilevicz, S. R. Upadhyaya, J. Batley, and D. Edwards, “Image-based Crop Disease Detection Using Machine Learning,” Plant Pathol., no. September 2024, pp. 18–38, 2024, doi: 10.1111/ppa.14006.
H. Brawijaya, E. Rahmawati, and T. Haryanto, “Optimization of Potato Leaf Disease Identification With Transfer Learning Approach Using Mobilenetv1 Architecture,” PILAR Nusa Mandiri J. Comput. Inf. Syst., vol. 20, no. 1, pp. 33–40, 2024, doi: 10.33480/pilar.v20i1.4718.
J. Fu, Y. Zhao, and G. Wu, “Potato Leaf Disease Segmentation Method Based on Improved UNet,” Appl. Sci., vol. 13, no. 20, 2023, doi: 10.3390/app132011179.
K. Shaheed et al., “EfficientRMT-Net—An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases,” Sensors, vol. 23, no. 23, 2023, doi: 10.3390/s23239516.
M. A. Naeem, M. A. Saleem, M. I. Sharif, S. Akbar, and M. Z. Sajid, “A Deep Learning model for the identication of Potato leaf diseases using Wrapper Feature Selection and Concatenation,” Res. Sq., pp. 1–20, 2024, doi: https://doi.org/10.21203/rs.3.rs-4155580/v1.
J. R. Aisya and A. Prasetiadi, “Klasifikasi Penyakit Daun Kentang dengan Metode CNN dan RNN,” J. Tekno Insentif, vol. 17, no. 1, pp. 1–10, 2023, doi: 10.36787/jti.v17i1.888.
A. K. Trivedi, T. Mahajan, T. Maheshwari, R. Mehta, and S. Tiwari, “Leveraging feature fusion ensemble of VGG16 and ResNet-50 for automated potato leaf abnormality detection in precision agriculture,” Soft Comput., pp. 0–21, 2025, doi: 10.1007/s00500-025-10523-0.
K. I. Roumeliotis, R. Sapkota, M. Karkee, N. D. Tselikas, and D. K. Nasiopoulos, “Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks,” IEEE Trans. Cybern., 2025.
B. Gülmez, A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture, no. 0123456789. Springer Netherlands, 2024. doi: 10.1007/s11540-024-09786-1.
S. Cong and Y. Zhou, “A Review of Convolutional Neural Network Architectures and Their Optimizations,” Artif. Intell. Rev., vol. 56, no. 3, pp. 1905–1969, 2023, doi: 10.1007/s10462-022-10213-5.
M. Krichen, “Convolutional Neural Networks: A Survey,” Computers, vol. 12, no. 8, pp. 1–41, 2023, doi: 10.3390/computers12080151.
W. Shafik, A. Tufail, C. Liyanage De Silva, and R. A. Awg Haji Mohd Apong, “A Novel Hybrid Inception-Xception Convolutional Neural Network for Efficient Plant Disease Classification and Detection,” Sci. Rep., vol. 15, no. 1, p. 3936, 2025, doi: 10.1038/s41598-024-82857-y.
A. A. Yahya, K. Liu, A. Hawbani, Y. Wang, and A. N. Hadi, “A Novel Image Classification Method Based on Residual Network, Inception, and Proposed Activation Function,” Sensors, vol. 23, no. 6, pp. 1–20, 2023, doi: 10.3390/s23062976.
Y. Pan et al., “Fundus Image Classification using Inception V3 and ResNet-50 for The Early Diagnostics of Fundus Diseases,” Front. Physiol., vol. 14, no. February, pp. 1–9, 2023, doi: 10.3389/fphys.2023.1126780.
F. M. Firnando, D. R. I. M. Setiadi, A. R. Muslikh, and S. W. Iriananda, “Analyzing InceptionV3 and InceptionResNetV2 with Data Augmentation for Rice Leaf Disease Classification,” J. Futur. Artif. Intell. Technol., vol. 1, no. 1, pp. 1–11, 2024, doi: 10.62411/faith.2024-4.
M. Neshat, M. Ahmed, H. Askari, M. Thilakaratne, and S. Mirjalili, “Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs,” Procedia Comput. Sci., vol. 235, pp. 1841–1850, 2024, doi: 10.1016/j.procs.2024.04.175.
G. Menghani, “Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better,” ACM Comput. Surv., vol. 55, no. 12, 2021, doi: 10.1145/3578938.
T. Nazir, M. M. Iqbal, S. Jabbar, A. Hussain, and M. Albathan, “EfficientPNet—An Optimized and Efficient Deep Learning Approach for Classifying Disease of Potato Plant Leaves,” Agric., vol. 13, no. 4, 2023, doi: 10.3390/agriculture13040841.
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, 2025, doi: 10.1007/s10462-024-11100-x.
L. Lisda, K. Kusrini, and D. Ariatmanto, “Classification of Pistachio Nut Using Convolutional Neural Network,” Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 8, no. 1, pp. 71–77, 2023, doi: 10.25139/inform.v8i1.5685.
M. Q. Kheder and A. A. Mohammed, “Transfer Learning Based Traffic Light Detection and Recognition Using CNN Inception-V3 Model,” Iraqi J. Sci., vol. 64, no. 10, pp. 5358–5375, 2023, doi: 10.24996/ijs.2023.64.10.40.
R. Shyam, “Performance Analysis of Deep CNN Architectures,” J. Commun. Eng. Syst., no. August, 2024, doi: 10.37591/JoCES.
T. R. Mahesh et al., “Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, 2024, doi: 10.1007/s44196-023-00397-1.
M. K. Anam, S. Defit, Haviluddin, L. Efrizoni, and M. B. Firdaus, “Early Stopping on CNN-LSTM Development to Improve Classification Performance,” J. Appl. Data Sci., vol. 5, no. 3, pp. 1175–1188, 2024, doi: 10.47738/jads.v5i3.312.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Khairun Nisa Arifin Nur, Nazlina Izmi Addyna, Agus Perdana Windarto, Anjar Wanto, Poningsih Poningsih
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
