Design of the "SRIKANDI" Image Processing Application for Detecting Rice Leaf Diseases using the NASNetMobile Convolutional Neural Network Architecture

Rice leaf diseases can cause a significant decrease in productivity if not treated early, while currently still using a manual diagnosis process that is often slow, inconsistent, and dependent on extension workers. In this study, the SRIKANDI application was developed, a mobile application for image processing for rice leaf diseases using the NASNetMobile Convolutional Neural Network (CNN) architecture. This system is designed using five labels, namely bacterial, blast, brownspot, leafsmut, and healthy leaves. The dataset used consists of 2500 images collected from Kaggle, Mendeley Data, and taken directly. All images go through preprocessing stages of resizing, pixel normalization, and augmentation, then divided into 80% train, 10% test, and 10% validation. The model training was carried out in two stages, namely, 40 epochs of fine-tuning with a learning rate of 0.0008 followed by 20 epochs of fine-tuning with a learning rate of 1e-5, the results obtained by the model with a test set accuracy rate of 96.40%. The trained model is then saved in TFLite format to be integrated into the SRIKANDI mobile application so that it can help farmers detect rice leaf diseases in real-time via camera or taken from the gallery.

Keywords: NASNetMobile, Convolutional Neural Network (CNN), Rice Leaf Disease Detection, Mobile Application

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[1] I. Journal, O. F. Latest, and T. In, “Optimizing Leafnet for Better Rice Leaf Disease Classification,” vol. XIV, no. Viii, pp. 724–730, 2025, doi: 10.51583/IJLTEMAS.

[2] H. K. K. Chinna Gopi Simhadri, “Automatic Recognition of Rice Leaf Diseases Using,” 2023, doi: 10.3390/agronomy13040961.

[3] M. N. Fawaiq, E. Utami, and D. Ariatmanto, “Rice Plant Disease Detection with Data Augmentation Using Transfer Learning International Journal of Research Publication and Reviews Rice Plant Disease Detection with Data Augmentation Using Transfer Learning,” no. April, 2023, doi: 10.55248/gengpi.2023.4.4.35530.

[4] D. J. Richter and K. Kim, “Assessing the performance of domain-specific models for plant leaf disease classification : a comprehensive benchmark of transfer-learning on open datasets,” pp. 1–31, 2025, doi: 10.1038/s41598-025-03235-w 1.

[5] R. Tiwari, J. Patel, N. R. Khan, and A. Dadhich, “automated identification of rice diseases,” pp. 1–16, 2025, doi: 10.1371/journal.pone.0307461.

[6] C. Gopi, H. Kishan, and V. Kumari, “Information Processing in Agriculture Deep learning for rice leaf disease detection : A systematic literature review on emerging trends , methodologies and techniques,” Inf. Process. Agric., vol. 12, no. 2, pp. 151–168, 2025, doi: 10.1016/j.inpa.2024.04.006.

[7] A. Y. Ashurov, M. S. A. M. Al-gaashani, N. A. Samee, R. Alkanhel, and G. Atteia, “Enhancing plant disease detection through deep learning : a Depthwise CNN with squeeze and excitation integration and residual skip connections,” no. January, pp. 1–16, 2025, doi: 10.3389/fpls.2024.1505857.

[8] K. Syah, K. Mayya, I. F. Makarim, Z. Yudi, and A. Darva, “Implementasi Metode Convolutional Neural Network untuk Memilah Foto Pemandangan Pantai dan Pegunungan,” vol. 2, no. 2, pp. 62–72, 2024, doi: 10.20895/jasmed.v2i2.1525.

[9] I. G. Rian, L. Oka, A. Agung, G. Bagus, W. S. Peradhayana, and N. L. Wiwik, “Performance Comparison of MobileNetV2 and NASNetMobile Architectures in Soybean Leaf Disease Classification,” vol. 6, no. 2, pp. 251–259, 2025, doi: 10.56705/ijodas.v6i2.243.

[10] J. Padhi, K. Mishra, A. Kumar, S. Kumari, P. K. Sethy, and A. Nanthaamornphong, “Smart Agricultural Technology Enhancing paddy leaf disease diagnosis -a hybrid CNN model using simulated thermal imaging,” Smart Agric. Technol., vol. 10, no. February, p. 100814, 2025, doi: 10.1016/j.atech.2025.100814.

[11] Y. K. Rathore, R. R. Janghel, C. Swarup, and S. K. Pandey, “Detection of rice plant disease from RGB and grayscale images using an LW17 deep learning model,” vol. 31, no. December 2022, pp. 2813–2833, 2023, doi: 10.3934/era.2023142.

[12] J. Ahamed, “A Decision-Making Technique for Software Architecture Design,” vol. 9, no. 4, pp. 43–49, 2023, doi: 10.5815/ijmsc.2023.04.05.

[13] V. Vranić et al., “Use case modeling in a research setting of developing an innovative pilgrimage support system,” Univers. Access Inf. Soc., vol. 23, no. 4, pp. 1543–1560, 2024, doi: 10.1007/s10209-023-01047-1.

[14] F. Siewe, “On the Execution and Runtime Verification of UML Activity Diagrams,” pp. 1–42, 2025, doi: 10.3390/software4010004.

[15] S. Syamala, A. Shrivastava, and S. Karmakar, “Social Sciences & Humanities Open Identifying interface design factors impacting user experience in digital learning platforms- A pilot study,” Soc. Sci. Humanit. Open, vol. 11, no. May, p. 101662, 2025, doi: 10.1016/j.ssaho.2025.101662.