Optimisasi VGG16 dengan Transfer Learning dalam Mendeteksi Penyakit Pada Daun Jagung
DOI:
https://doi.org/10.47065/bulletincsr.v5i5.631Keywords:
CNN; Image Classification; Corn Leaf Disease; Transfer Learning; VGG16Abstract
Corn is one of the major agricultural commodities that plays a strategic role in national food security. However, its productivity often declines due to leaf diseases such as Blight, Common Rust, and Gray Leaf Spot. Manual disease detection is considered inefficient and prone to human error, especially on a large scale. This study aims to develop an automated deep learning-based system for accurate classification of corn leaf diseases. The proposed model utilizes the Convolutional Neural Network (CNN) architecture VGG16 with a transfer learning approach. The dataset comprises 1,200 labeled images of corn leaves categorized into four disease classes, obtained from Kaggle. Image augmentation techniques were applied to improve data diversity and enhance model generalization. The performance of VGG16 was compared with VGG16 Baseline architecture and MobileNetV2. Experimental results show that VGG16 with transfer learning achieved the highest classification accuracy of 96.25%, outperforming the baseline VGG16 (92.92%) and MobileNetV2 (84.58%). These findings demonstrate the effectiveness of VGG16-based transfer learning in automating corn leaf disease detection, supporting the implementation of precision agriculture technology.
Downloads
References
I. P. Putra, R. Rusbandi, dan D. Alamsyah, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network,” J. Algoritm., vol. 2, no. 2, hal. 102–112, Apr 2022, doi: 10.35957/algoritme.v2i2.2360.
D. Iswantoro dan D. Handayani UN, “Klasifikasi Penyakit Tanaman Jagung Menggunakan Metode Convolutional Neural Network (CNN),” J. Ilm. Univ. Batanghari Jambi, vol. 22, no. 2, hal. 900, 2022, doi: 10.33087/jiubj.v22i2.2065.
P. Theerthagiri, A. U. Ruby, J. G. C. Chandran, T. H. Sardar, dan A. Shafeeq B. M, “Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00972-z.
C. Bi, S. Xu, N. Hu, S. Zhang, Z. Zhu, dan H. Yu, “Identification Method of Corn Leaf Disease Based on Improved Mobilenetv3 Model,” Agronomy, vol. 13, no. 2, hal. 1–17, 2023, doi: 10.3390/agronomy13020300.
R. L. De Rossi et al., “Crop damage, economic losses, and the economic damage threshold for northern corn leaf blight,” Crop Prot., vol. 154, hal. 105901, Apr 2022, doi: 10.1016/j.cropro.2021.105901.
M. A. H. Foysal, F. Ahmed, dan M. Z. Haque, “Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration,” 2024, doi: 10.5120/ijca2024924026.
M. Yusuf, D. Kurniawan, dan T. Agustin, “Klasifikasi Penyakit Tanaman Jagung dengan Kecerdasan Buatan Berbasis CNN,” Semin. Nas. AMIKOM SURAKARTA 2024, no. November, hal. 355–368, 2024.
J. O. Olayiwola dan J. A. Adejoju, “Maize (Corn) Leaf Disease Detection System Using Convolutional Neural Network (CNN),” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), O. Gervasi, B. Murgante, D. Taniar, B. O. Apduhan, A. C. Braga, C. Garau, dan A. Stratigea, Ed., Cham: Springer Nature Switzerland, 2023, hal. 309–321. doi: 10.1007/978-3-031-36805-9_21.
R. Singh, C. Prabha, M. Malik, dan A. Goyal, “A Robust Deep Learning Model for Brain Tumor Detection and Classification Using Efficient Net: A Brief Meta-Analysis,” J. Adv. Res. Appl. Sci. Eng. Technol., vol. 49, no. 2, hal. 26–51, Agu 2025, doi: 10.37934/araset.49.2.2651.
K. Adem, E. Kavalc? Y?lmaz, F. Ölmez, K. Çelik, dan H. Bak?r, “A Comparative Analysis of Deep Learning Parameters for Enhanced Detection of Yellow Rust in Wheat,” Uluslararas? Muhendis. Arastirma ve Gelistirme Derg., vol. 16, no. 02, hal. 659–667, 2024, doi: 10.29137/umagd.1390763.
S. Solikhun, A. P. Windarto, dan P. Alkhairi, “Bone fracture classification using convolutional neural network architecture for high-accuracy image classification,” Int. J. Electr. Comput. Eng., vol. 14, no. 6, hal. 6466–6477, Des 2024, doi: 10.11591/ijece.v14i6.pp6466-6477.
N. W. Utami, I Nyoman Purnama, dan I Putu Restu Prajna, “Klasifikasi Tanaman Upakara Adat Hindu Di Kebun Raya Eka Karya Bali Menggunakan Algoritma Convolutional Neural Network,” J. Inform. Teknol. dan Sains, vol. 5, no. 4, hal. 671–678, 2023, doi: 10.51401/jinteks.v5i4.3416.
M. Alkanan dan Y. Gulzar, “Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning,” Front. Appl. Math. Stat., vol. 9, hal. 1–12, 2023, doi: 10.3389/fams.2023.1320177.
M. N. Qureshi, M. S. Umar, dan S. Shahab, “A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification,” Computers, vol. 11, no. 5, 2022, doi: 10.3390/computers11050064.
K. I. Nauval dan S. Lestari, “Implementasi Deteksi Objek Penyakit Daun Kentang dengan Metode Convolutional Neutral Network,” J. Apl. Teknol. Inf. dan Manaj., vol. 3, no. 2, hal. 136–149, 2022, doi: 10.31102/jatim.v3i2.1576.
C. ÖZDEN, “Corn Disease Detection Using Transfer Learning,” Black Sea J. Eng. Sci., vol. 6, no. 4, hal. 387–393, 2023, doi: 10.34248/bsengineering.1322907.
S. Ahmmed et al., “Enhancing Brain Tumor Classification with Transfer Learning across Multiple Classes: An In-Depth Analysis,” BioMedInformatics, vol. 3, no. 4, hal. 1124–1144, Des 2023, doi: 10.3390/biomedinformatics3040068.
I. Khan, S. S. Sohail, D. Ø. Madsen, dan B. K. Khare, “Deep transfer learning for fine-grained maize leaf disease classification,” J. Agric. Food Res., vol. 16, no. 4, hal. 1–9, 2024, doi: 10.1016/j.jafr.2024.101148.
Mochammad Faisal Nur Sayyid, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode CNN Dengan Image Processing HE Dan CLAHE,” J. Tek. Inform. dan Teknol. Inf., vol. 4, no. 1, hal. 86–95, Jan 2024, doi: 10.55606/jutiti.v4i1.3425.
A. Rachmad, M. Fuad, dan E. M. S. Rochman, “Convolutional Neural Network-Based Classification Model of Corn Leaf Disease,” Math. Model. Eng. Probl., vol. 10, no. 2, hal. 530–536, 2023, doi: 10.18280/mmep.100220.
Q. N. Azizah, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network AlexNet,” sudo J. Tek. Inform., vol. 2, no. 1, hal. 28–33, 2023, doi: 10.56211/sudo.v2i1.227.
A. A. Kadam, H. Ganatra, A. R. Sawant, dan Y. Sivramkrishnan, “Corn Leaves Disease Detection Using Convolutional Neural Networks [CNN],” Interantional J. Sci. Res. Eng. Manag., vol. 07, no. 10, hal. 1–11, 2023, doi: 10.55041/ijsrem26275.
B. D. Mardiana, W. B. Utomo, U. N. Oktaviana, G. W. Wicaksono, dan A. E. Minarno, “Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 1, hal. 20–26, 2023, doi: 10.29207/resti.v7i1.4550.
M. O. Lawrence, “SqueezeNet Deep Learning Model for Magnetic Resonance Imaging Brain Tumor Detection,” Fac. Nat. Appl. Sci. J. …, vol. 6, no. 1, hal. 72–83, 2024, [Daring]. Tersedia pada: https://fnasjournals.com/index.php/FNAS-JSI/article/view/559
Y. Gulzar, “Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique,” Sustain., vol. 15, no. 3, hal. 2–14, 2023, doi: 10.3390/su15031906.
N. F. Hikmah, A. D. Hajjanto, A. F. A. Surbakti, N. A. Prakosa, T. Asmaria, dan T. A. Sardjono, “Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels,” Int. J. Electr. Comput. Eng., vol. 14, no. 4, hal. 3995–4004, Agu 2024, doi: 10.11591/ijece.v14i4.pp3995-4004.
J. Kusuma, Rubianto, R. Rosnelly, Hartono, dan B. H. Hayadi, “Klasifikasi Penyakit Daun Pada Tanaman Jagung Menggunakan Algoritma Support Vector Machine, K-Nearest Neighbors dan Multilayer Perceptron,” J. Appl. Comput. Sci. Technol., vol. 4, no. 1, hal. 1–6, 2023, doi: 10.52158/jacost.v4i1.484.
A. Sharma, A. Zehra, A. Das, K. Rastogi, dan ..., “Brain Tumor Classification: A Comparison Study CNN, VGG 16 and ResNet50 Model,” … Conf. Data …, 2023, [Daring]. Tersedia pada: https://ieeexplore.ieee.org/abstract/document/10245710/
S. Sharma dan K. Guleria, “A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks,” 2022. doi: 10.1016/j.procs.2023.01.018.
A. Achmadi, B. A. Umam, dan A. Anwari, “Identifikasi Penyakit Daun Tembakau Berbasis Pengolahan Citra dengan Metode Convolutional Neural Network ( CNN ) Dan Metode Transfer Learning,” 2024.
G. Batchuluun, S. H. Nam, dan K. R. Park, “Deep Learning-Based Plant-Image Classification Using a Small Training Dataset,” Mathematics, vol. 10, no. 17, hal. 1–26, 2022, doi: 10.3390/math10173091.
S. Saeedi, S. Rezayi, H. Keshavarz, dan S. R. Niakan Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Med. Inform. Decis. Mak., vol. 23, no. 1, hal. 16, Jan 2023, doi: 10.1186/s12911-023-02114-6.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Optimisasi VGG16 dengan Transfer Learning dalam Mendeteksi Penyakit Pada Daun Jagung
ARTICLE HISTORY
Issue
Section
Copyright (c) 2025 Ade Ismiaty Ramadhona Ht. Barat, Wiwik Sri Astuti, Anjar Wanto, Solikhun Solikhun
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
