INVENTARISASI ALGORITMA DEEP LEARNING DALAM DETEKSI PENYAKIT DAUN TEBU
DOI:
https://doi.org/10.51903/ccz51528Keywords:
deep learning, deteksi penyakit tebu, convolutional neural network, vision transformerAbstract
Tebu (Saccharum spp.) merupakan komoditas industri utama yang produktivitasnya sangat dipengaruhi oleh penyakit daun, sehingga deteksi dini dan akurat menjadi faktor penting dalam menjaga keberlanjutan produksi. Deep learning menawarkan solusi canggih dan non-destruktif untuk diagnosis penyakit melalui analisis citra secara otomatis. Namun, penelitian yang ada mengenai deteksi penyakit daun tebu masih bersifat terfragmentasi, dengan variasi pada dataset, metode praproses, serta metrik evaluasi yang menyulitkan perbandingan langsung antar algoritma.
Penelitian ini secara sistematis melakukan inventarisasi dan evaluasi algoritma deep learning yang diterapkan pada deteksi penyakit daun tebu, termasuk Convolutional Neural Networks (CNN), Vision Transformers (ViT), dan arsitektur hibrida CNN–Transformer. Sintesis komparatif terhadap berbagai model pretrained yang banyak digunakan—seperti ResNet, EfficientNet, DenseNet, dan MobileNet—dilakukan untuk mengidentifikasi kinerja relatif serta adaptabilitasnya terhadap citra lapangan dan UAV. Studi ini juga mengusulkan rekomendasi metodologis terstandar terkait praproses, augmentasi, dan evaluasi guna meningkatkan replikasi serta penerapan di tingkat lapangan.
Hasil penelitian ini menjembatani kesenjangan antara kemajuan teoretis dan aplikasi praktis dalam pertanian presisi, serta memberikan kerangka acuan bagi peneliti dan praktisi untuk memilih pendekatan deep learning yang paling sesuai dalam pemantauan kesehatan tebu di kondisi nyata.
Downloads
References
Aboelenin, Sherihan, Foriaa Ahmed, Elbasheer Mohamed, and Meselhy Eltoukhy. (2025). “A Hybrid Framework for Plant Leaf Disease Detection and Classification Using Convolutional Neural Networks and Vision Transformer.”
Afiqah, Nik, and N Ahmad Yani. (2024). “A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach” 10, no. 2: 232–49.
Barman, Utpal, Parismita Sarma, Mirzanur Rahman, Vaskar Deka, Swati Lahkar, and Vaishali Sharma. 2024. “ViT-SmartAgri : Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture.”
Dadabhau, Swapnil, and Sanjay M Koli. (2024). “Enhanced Deep Learning Technique for Sugarcane Leaf Disease Classification and Mobile Application Integration.” Heliyon 10, no. 8: e29438. https://doi.org/10.1016/j.heliyon.2024.e29438.
Das, Bappaditya, Chandan Das, and C S Raghuvanshi. (2024). “Transfer Learning Boosts Ensembles for Precise Sugarcane Leaf Disease Detection” 5, no. 4: 2039–53.
Dhaka, Vijaypal Singh, Sangeeta Vaibhav Meena, Geeta Rani, Deepak Sinwar, Kavita, Muhammad Fazal Ijaz, and Marcin Woźniak. (2021). “A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.” Sensors 21, no. 14. https://doi.org/10.3390/s21144749.
Hassan, Sk Mahmudul, Khwairakpam Amitab, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska, Tomas Novak, and Arnab Kumar Maji. (2022). “A Survey on Different Plant Diseases Detection Using Machine Learning Techniques,” 1–29.
Hassan, M., Ali, T., & Chaudhry, H. (2023). A survey on deep neural networks for precision agriculture: Models, datasets, and open challenges. Information Processing in Agriculture, 10(4), 567–582.
Ismail Kunduracioglu, Ishak Pacal. (2024). “Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models.”
Izza, Mufidatul, and Moch Lutfi. (2025). “Detection of Sugarcane Leaf Disease Using Pre-Trained Feature Extraction and SVM Method” 9, no. 5: 2296–2302.
Kavitha, K J, and Krishna Prasad K. (2024). “CNN Ensemble Approach for Early Detection of Sugarcane Diseases – a Comparison.” INTL JOURNAL OF ELECTRONICS AND TELECOMMUNICATIONS 70, no. 2: 455–64. https://doi.org/10.24425/ijet.2024.149566.
Kumar, P., Sharma, R., & Mehta, V. (2024). Comparative evaluation of lightweight CNNs for sugarcane disease detection under field conditions. Journal of Plant Pathology and Microbiology, 15(2), 87–99.
Kunduracıoğlu, İ. (2024). Deep Learning-Based Disease Detection in Sugarcane. JOPI Journal.
Mehdipour, P., Akbari, A., & Lee, S. H. (2024). Vision Transformer applications in smart agriculture: From detection to diagnosis. IEEE Access, 12, 132556–132573.
Nyawose, Thandiwe, Rito Clifford Maswanganyi, and Philani Khumalo. (2025). “A Review on the Detection of Plant Disease Using MachineLearning and Deep Learning Approaches,” 1–37.
Page, M. J., et al. (2021). The PRISMA (2020) statement: An updated guideline for reporting systematic reviews. BMJ, 372:n71.
Rahman, M., Alam, M., & Saha, S. (2022). A comprehensive study of deep learning architectures for leaf disease classification in tropical crops. MDPI Agriculture, 12(11), 1784.
Raji N. (2024). Deep Learning Methods for Identifying Diseases in Plants: A Survey. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 860–867.
Singh, R., Kumar, A., & Gupta, S. (2024). Transformer-based hybrid models for agricultural disease classification using UAV imagery. Expert Systems with Applications, 243, 123910.
Srinivasan, Saravanan, S M Prabin, Sandeep Kumar Mathivanan, Hariharan Rajadurai, and Suresh Kulandaivelu. (2025). “Sugarcane Leaf Disease Classification Using Deep Neural Network Approach.”
Thakur, Poornima Singh, Pritee Khanna, Tanuja Sheorey, and Aparajita Ojha. (2022). “E XPLAINABLE VISION TRANSFORMER ENABLED CONVOLUTIONAL NEURAL NETWORK FOR PLANT DISEASE IDENTIFICATION : P LANT XV I T,” no. Dl. https://doi.org/org/10.48550/arXiv.2207.07919.
Upadhye, Sammed Abhinandan, Maneetkumar Rangnath Dhanvijay, and Sudhir Madhav Patil. (2023). “Sugarcane Disease Detection Using CNN-Deep Learning Method : An Indian Perspective” 11, no. 1: 80–97. https://doi.org/10.13189/ujar.2023.110108.
Verry Riyanto1,*, Sri Nurdiati2, Marimin2, Muhamad Syukur2, Shelvie Nidya Neyman. (2025). “Deep Learning Approaches for Plant Disease Diagnosis Systems: A Review and Future Research Agendas” 9, no. 2: 185–201. https://doi.org/org/10.55043/jaast.v9i2.308.
Yani, N., et al. (2024). A Systematic Literature Review on Leaf Disease Recognition. JISEBI (UNAIR).
Zhao, X., Zhang, Y., & Wang, J. (2023). Deep learning-based plant disease detection: A review of advances and challenges. Computers and Electronics in Agriculture, 210, 107987.
Zhou, T., & Lin, Y. (2023). Benchmarking deep learning methods for real-world plant disease detection. Computers and Electronics in Agriculture, 206, 107762
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
