DETECTION OF SUGARCANE LEAF DISEASES USING MOBILENETV3LARGE-BASED TRANSFER LEARNING FOR MOBILE APPLICATIONS
DOI:
https://doi.org/10.34288/jri.v8i3.542Keywords:
Sugarcane, Leaf Disease Detection, MobileNetV3Large, Transfer learning, Mobile ApplicationAbstract
Sugarcane is one of Indonesia's key plantation commodities with a critical role in fulfilling national sugar demand and supporting bioethanol production. However, sugarcane productivity remains low due to leaf diseases that reduce crop quality and yields, while slow or inaccurate identification accelerates their spread. This study proposes and develops a mobile-based sugarcane leaf disease detection system using transfer learning with the MobileNetV3Large architecture to classify 11 disease classes. Two dataset scenarios were applied: Scenario 1 using the SLD Dataset with 6,748 images and Scenario 2 combining the SLD and Sugarcane Smut datasets totaling 14,804 images. Each scenario was trained under three optimizer configurations: Adam, RMSprop, and SGD, to identify the best-performing combination. Results show that Adam achieved the highest validation accuracy in both scenarios, reaching 94.24% in Scenario 1 and 97.43% in Scenario 2, with corresponding test accuracies of 94.91% and 97.31% respectively. The final model was deployed as a Flutter-based mobile application capable of performing real-time disease detection through image upload or camera capture, providing an accessible tool for farmers to identify sugarcane leaf diseases efficiently.
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Ady, R. P., & Pramudwiatmoko, A. (2025). APPLICATION OF TRANSFER LEARNING ON EFFICIENTNET-B0 ARCHITECTURE FOR AUTOMATIC ROOF TILE DAMAG CLASSIFICATION. Jurnal Riset Informatika, 8(1), 63–73. https://doi.org/10.34288/jri.v8i1.450
Agustiani, S., Aryanti, R., Khotimatul Wildah, S., Arifin, Y. T., Marlina, S., & Misriati, T. (2024). Optimisasi Model Deep Learning untuk Deteksi Penyakit Daun Tebu dengan Fine-Tuning MobileNetV2. Journal of Informatics Management and Information Technology, 4(4), 150–157. https://doi.org/10.47065/jimat.v4i4.411
Amrulloh, I. T. A., Nurina Sari, B., Nur Padilah Informatika, T., Singaperbangsa Karawang, U., HSRonggo Waluyo, J., Telukjambe Timur, K., Karawang, K., & Barat, J. (2024). EVALUASI AUGMENTASI DATA PADA DETEKSI PENYAKIT DAUN TEBU DENGAN YOLOV8. Jurnal Mahasiswa Teknik Informatika, 8(4), 7547–7552. https://doi.org/https://doi.org/10.36040/jati.v8i4.10267
Azhari, W. F., & Purnomo, D. (2022). Analisis input – output: Dampak sektor pertanian terhadap perekonomian, pendapatan rumah tangga, dan kesesempatan kerja. Journal of Economics Research and Policy Studies, 2(3). https://doi.org/10.53088/jerps.v2i3.417
Danso-Boateng, E., & Achaw, O. W. (2022). Bioenergy and biofuel production from biomass using thermochemical conversions technologies—a review. AIMS Energy, 10(4), 585–647. https://doi.org/10.3934/energy.2022030
Daphal, S. D., & Koli, S. M. (2024). Enhanced Classification of Sugarcane Diseases Through a Modified Learning Rate Policy in Deep Learning. Traitement Du Signal, 41(1), 441–449. https://doi.org/10.18280/ts.410138
Faqih, R. R., Irsan, M., & Fathoni, M. F. (2024). Rice Plant Disease Detection System Using Transfer Learning with MobilenetV3Large. Sinkron, 8(2), 805–812. https://doi.org/10.33395/sinkron.v8i2.13383
Ilham, I. M., Ernawati, S., & Indra, M. (2024). IMPROVING IMAGE CLASSIFICATION ACCURACY WITH OVERSAMPLING AND DATA AUGMENTATION USING DEEP LEARNING: A CASE STUDY ON THE SIMPSONS CHARACTERS DATASET. Jurnal Riset Informatika, 6(4), 201–210. https://doi.org/10.34288/jri.v6i4.348
Khan, A. T., Jensen, S. M., Khan, A. R., & Li, S. (2023). Plant disease detection model for edge computing devices. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1308528
Khan, F., Zafar, N., Tahir, M. N., Aqib, M., Waheed, H., & Haroon, Z. (2023). A mobile-based system for maize plant leaf disease detection and classification using deep learning. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1079366
Kunduracioglu, I., & Pacal, I. (2024). Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models. Journal of Operations Intelligence, 2(1), 321–335. https://doi.org/10.31181/jopi21202423
Mazumder, M. K. A., Mridha, M. F., Alfarhood, S., Safran, M., Abdullah-Al-Jubair, M., & Che, D. (2023). A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1321877
Msomba, B. H., Ndaki, P. M., & Joseph, C. O. (2024). Sugarcane sustainability in a changing climate: a systematic review on pests, diseases, and adaptive strategies. In Frontiers in Agronomy (Vol. 6). Frontiers Media SA. https://doi.org/10.3389/fagro.2024.1423233
Mudhoffar, K., & Magriasti, L. (2024). Ekonomi Politik Energi Terbarukan: Peluang dan Tantangan di Indonesia. Multiverse: Open Multidisciplinary Journal, 3(1), 47–52. https://doi.org/10.57251/multiverse.v3i1.1382
Nurdiansyah, Setyani, M., Sespira, D., Anggiriani, F., Aqbal, J., Erlangga, M. B., Pratiwi, M. M. A., Meilani, D., Andri, R. Z., Triansyah, R. P., & Saputra, Y. (2024). 49.+Production_Nurdiansyah (1). Jurnal Pengabdian Masyarakat Bangsa, 2(7), 2774–2780. https://doi.org/https://doi.org/10.59837/jpmba.v2i7.1334
Parascanu, M. M., Sanchez, N., Sandoval-Salas, F., Carreto, C. M., Soreanu, G., & Sanchez-Silva, L. (2021). Environmental and economic analysis of bioethanol production from sugarcane molasses and agave juice. Environmental Science and Pollution Research, 28(45), 64374–64393. https://doi.org/10.1007/s11356-021-15471-4
Pramudhita, D. A., Azzahra, F., Arfat, I. K., Magdalena, R., & Saidah, S. (2023). Strawberry Plant Diseases Classification Using CNN Based on MobileNetV3-Large and EfficientNet-B0 Architecture. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 9(3), 522–534. https://doi.org/10.26555/jiteki.v9i3.26341
Rajput, M. A., Rajput, N. A., Syed, R. N., Lodhi, A. M., & Que, Y. (2021). Sugarcane smut: Current knowledge and the way forward for management. In Journal of Fungi (Vol. 7, Number 12). MDPI. https://doi.org/10.3390/jof7121095
Respati, E. (2022). OUTLOOK TEBU 2022 Pusat Data dan Sistem Informasi Pertanian i OUTLOOK TEBU Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal-Kementerian Pertanian 2022.
Rifkiansyah, H., & Azrino Gustalika, M. (2025). Design And Development Of A Ticket Booking Application Using Extreme Programming At Serayu Larangan. Jurnal Riset Informatika, 7(4), 278–288. https://doi.org/10.34288/jri.v7i4.384
Shafik, W., Tufail, A., De Silva Liyanage, C., & Apong, R. A. A. H. M. (2024). Using transfer learning-based plant disease classification and detection for sustainable agriculture. BMC Plant Biology, 24(1). https://doi.org/10.1186/s12870-024-04825-y
Shoaib, M., Shah, B., EI-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., Gechev, T., Hussain, T., & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. In Frontiers in Plant Science (Vol. 14). Frontiers Media SA. https://doi.org/10.3389/fpls.2023.1158933
Sitompul, D., Lumbantobing, P., Manik, S., & Harefa, M. S. (2024). Optimasi Penggunaan Bio-Pestisida sebagai Pengganti Pestisida Kimia pada Pertanian di Kec. Percut Sei Tuan, Kab. Deli Serdang. El-Mujtama: Jurnal Pengabdian Masyarakat , 4(2). https://doi.org/10.47467/elmujtama.v4i2.1281
Strachan, S., Bhuiyan, S. A., Thompson, N., Nguyen, N. T., Ford, R., & Shiddiky, M. J. A. (2022). Latent potential of current plant diagnostics for detection of sugarcane diseases. In Current Research in Biotechnology (Vol. 4, pp. 475–492). Elsevier B.V. https://doi.org/10.1016/j.crbiot.2022.10.002
Wantoro, A., Dwi Feriyanto, Arry Verdian, & Rakhmat Dedi Gunawan. (2025). Implementasi Algoritma Machine Learning untuk Deteksi Penyakit Daun Tebu: Analisis Perbandingan Kinerja. Prosiding Seminar Nasional Pembangunan Dan Pendidikan Vokasi Pertanian, 6(1), 1683–1690. https://doi.org/10.47687/snppvp.v6i1.1902
Xu, M., Yoon, S., Jeong, Y., & Park, D. S. (2022). Transfer learning for versatile plant disease recognition with limited data. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.1010981
Xu, W., Xu, T., Alex Thomasson, J., Chen, W., Karthikeyan, R., Tian, G., Shi, Y., Ji, C., & Su, Q. (2023). A lightweight SSV2-YOLO based model for detection of sugarcane aphids in unstructured natural environments. Computers and Electronics in Agriculture, 211. https://doi.org/10.1016/j.compag.2023.107961
Yunizar, S. F., Sari, A. P., & Aditiawan, F. P. (2025). Pemanfaatan Model ResNet50 dan SVM untuk Klasifikasi Penyakit Daun Tebu. CICES, 11(1), 94–104. https://doi.org/10.33050/cices.v11i1.3506
Zhang, Y., Wa, S., Zhang, L., & Lv, C. (2022). Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.875693
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