Corn Leaf Disease Classification Using MobileNetV2 and TensorFlow Lite for Mobile Deployment in Semangga District Merauke
Keywords:
Deep learning, MobileNetV2, transfer learning, corn leaf disease, TensorFlow Lite
Abstract
Deep learning-based corn leaf disease detection studies on the PlantVillage dataset commonly report high accuracy but rarely evaluate comprehensive model efficiency for mobile deployment across conversion formats. This study implements transfer learning with MobileNetV2 architecture for classifying four corn leaf conditions using the PlantVillage dataset (4,188 images), followed by TensorFlow Lite conversion as preparation for Android application deployment. The model was trained using a two-phase approach (feature extraction and fine-tuning). Evaluation results show 91.69% accuracy with a macro F1-score of 90.18%. Confusion matrix analysis reveals three findings: (1) perfect 100% precision on the Healthy class with zero false negatives, guaranteeing the model does not misclassify diseased leaves as healthy; (2) Common Rust as the main confuser due to training data imbalance; and (3) classification confusion between Northern Leaf Blight and Gray Leaf Spot, consistent with their visual symptom similarity. Quantized TFLite conversion yields 89.93% size reduction (25.26 MB to 2.55 MB) without significant accuracy loss. This study serves as a methodological baseline before local dataset collection in Semangga District, Merauke.
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[20] J. M. J. Ward, E. L. Stromberg, D. C. Nowell, and F. W. Nutter Jr, “Gray leaf spot: a disease of global importance in maize production,” Plant Dis., vol. 83, no. 10, pp. 884–895, 1999.
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[2] F. Khan, N. Zafar, M. N. Tahir, M. Aqib, H. Waheed, and Z. Haroon, “A mobile-based system for maize plant leaf disease detection and classification using deep learning,” Front. Plant Sci., vol. Volume 14-2023, 2023, [Online]. Available: https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1079366
[3] I. Pacal, “Enhancing crop productivity and sustainability through disease identification in maize leaves: Exploiting a large dataset with an advanced vision transformer model,” Expert Syst. Appl., vol. 238, p. 122099, 2024, doi: https://doi.org/10.1016/j.eswa.2023.122099.
[4] M. Shoaib et al., “An advanced deep learning models-based plant disease detection: A review of recent research,” Front. Plant Sci., vol. 14, p. 1158933, 2023.
[5] I. Pacal et al., “A systematic review of deep learning techniques for plant diseases,” Artif. Intell. Rev., vol. 57, no. 11, p. 304, 2024.
[6] M. Tariq et al., “Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI,” Front. Plant Sci., vol. Volume 15-2024, 2024, [Online]. Available: https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1402835
[7] G. Sharma, V. Anand, R. Chauhan, N. Garg, and S. Gupta, “Leaf Watch: An Innovative Multi-Class Pre-Trained Model for Untying the Spectrum of Maize Leaf Diseases,” in 2023 3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023, 2023. doi: 10.1109/SMARTGENCON60755.2023.10442289.
[8] M. Alkanan and Y. Gulzar, “Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning,” Front. Appl. Math. Stat., vol. Volume 9-2023, 2024, [Online]. Available: https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2023.1320177
[9] S. Aboelenin, F. A. Elbasheer, M. M. Eltoukhy, W. M. El-Hady, and K. M. Hosny, “A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer,” Complex and Intelligent Systems, vol. 11, no. 2, 2025, doi: 10.1007/s40747-024-01764-x.
[10] J. Nakatumba-Nabende and S. Murindanyi, “Deep learning models for enhanced in-field maize leaf disease diagnosis,” Machine Learning with Applications, vol. 20, p. 100673, 2025, doi: https://doi.org/10.1016/j.mlwa.2025.100673.
[11] J. Liu, C. He, Y. Jiang, M. Wang, Z. Ye, and M. He, “A high-precision identification method for maize leaf diseases and pests based on LFMNet under complex backgrounds,” Plants, vol. 13, no. 13, p. 1827, 2024.
[12] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520. doi: 10.1109/CVPR.2018.00474.
[13] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A Survey on Deep Transfer Learning,” in Artificial Neural Networks and Machine Learning – ICANN 2018, V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, and I. Maglogiannis, Eds., Cham: Springer International Publishing, 2018, pp. 270–279.
[14] A. Y. Ashurov et al., “Enhancing plant disease detection through deep learning: a Depthwise CNN with squeeze and excitation integration and residual skip connections,” Front. Plant Sci., vol. 15, p. 1505857, 2025.
[15] R. Sharma, M. Mittal, V. Gupta, and D. Vasdev, “Detection of plant leaf disease using advanced deep learning architectures,” International Journal of Information Technology, vol. 16, no. 6, pp. 3475–3492, 2024.
[16] B. Jacob et al., “Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2704–2713, 2017, [Online]. Available: https://api.semanticscholar.org/CorpusID:39867659
[17] D. Tejaswi, T. Sri Vaishnavi, B. Nandini, P. Nuka Raju, and D. Jayanth Babu, “Plant disease detection using deep learning,” International Journal of Science and Research Archive, vol. 12, no. 1, pp. 2476–2488, Jun. 2024, doi: 10.30574/ijsra.2024.12.1.1043.
[18] S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Front. Plant Sci., vol. Volume 7-2016, 2016, [Online]. Available: https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2016.01419
[19] A. A. Ahmed and G. H. Reddy, “A mobile-based system for detecting plant leaf diseases using deep learning,” AgriEngineering, vol. 3, no. 3, pp. 478–493, 2021.
[20] J. M. J. Ward, E. L. Stromberg, D. C. Nowell, and F. W. Nutter Jr, “Gray leaf spot: a disease of global importance in maize production,” Plant Dis., vol. 83, no. 10, pp. 884–895, 1999.
[21] A. S. Hatem, M. S. Altememe, and M. A. Fadhel, “Identifying corn leaves diseases by extensive use of transfer learning: a comparative study,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 2, pp. 1030–1038, 2023.
[22] M. Masood et al., “MaizeNet: A deep learning approach for effective recognition of maize plant leaf diseases,” IEEE Access, vol. 11, pp. 52862–52876, 2023.
[23] F. Rajeena PP, A. SU, M. A. Moustafa, and M. A. S. Ali, “Detecting plant disease in corn leaf using efficientnet architecture—an analytical approach,” Electronics (Basel)., vol. 12, no. 8, p. 1938, 2023.
[24] I. Pacal and G. Işık, “Utilizing convolutional neural networks and vision transformers for precise corn leaf disease identification,” Neural Comput. Appl., vol. 37, no. 4, pp. 2479–2496, 2025.
Published
2026-04-30
How to Cite
Budiasto, J., Jayawardana, H., Rahayu, T. K., & Putra, N. P. (2026). Corn Leaf Disease Classification Using MobileNetV2 and TensorFlow Lite for Mobile Deployment in Semangga District Merauke. MUSTEK ANIM HA, 15(01), 26 - 34. https://doi.org/10.35724/mustek.v15i01.7576
Section
Article
