Chili Leaf Disease Classification Using Transfer Learning with VGG16 and MobileNetV2 Combined with Random Search Hyperparameter Tuning
DOI:
https://doi.org/10.5281/zenodo.17383224Keywords:
Chili Plant, Machine Learning, CNN, Transfer Learning, MobileNet V2, VGG16, Random SearchAbstract
Chili is one of the main food commodities in Indonesia with considerable economic value. Frequent climate changes have made chili plants more vulnerable to pest and disease attacks. Early identification of these diseases is crucial, as delays can lead to crop failure. However, this process presents its own challenges, as it requires specific expertise and considerable time. This study employs the transfer learning method using the VGG16 and MobileNetV2 architectures to build a model capable of classifying diseases in chili plants based on leaf images, along with the use of Random Search hyperparameter tuning to improve model accuracy. The results show that the use of transfer learning for disease classification achieved high accuracy, with MobileNetV2 reaching an accuracy score of 88% without tuning. Meanwhile, the application of Random Search hyperparameter tuning proved effective in improving model accuracy, particularly with the VGG16 architecture, which saw a significant accuracy increase from 51% to 89%. It can be concluded that the transfer learning method is well-suited for identifying diseases in chili plants based on leaf images with high accuracy, and that the application of Random Search hyperparameter tuning successfully enhanced the model’s performance.
Downloads
References
Anafiotika, R., Suparman, S., Fauziah, Z., Meira Zhafirah, A., Margareta, G., Dwi Rani, F., Wardani, A., Tegar Yusniawan, M., Studi Proteksi Tanaman Jurusan Hama dan Penyakit Tumbuhan, P., & Pertanian, F. (2023). Intensitas Serangan Hama Dan Penyakit Cabai Rawit Di Provinsi Sumatera Selatan. Seminar Nasional Pertanian Pesisir, 2(1), 535–547.
Asir, M., Wahab, A., Yani, N. F., Arum, R. A., & Ramlah, R. (2023). Strategi peningkatan penjualan produk pertanian cabai di Kabupaten Sinjai. JPPI (Jurnal Penelitian Pendidikan Indonesia), 9(2), 725. https://doi.org/10.29210/020231803
Badan Pangan Nasional. (2024). Badan Pangan Nasional - Home. https://badanpangan.go.id/
Emmanuel, T. O. (2018). PlantVillage Dataset. Kaggle. https://www.kaggle.com/datasets/emmarex/plantdisease
Hasanah, L. (2022). Analisis Faktor-Faktor Pengaruh Terjadinya Impor Beras di Indonesia Setelah Swasembada Pangan. Growth: Jurnal Ilmiah Ekonomi Pembangunan, 1(2), 57–72. https://e-journal.unimaju.ac.id/index.php/GJIEP/article/view/6
Hubballi, M. (2023). chilli_leaf_dataset. Kaggle. https://www.kaggle.com.datasets/mahaninghubballi/chilli-leaf-dataset
Iswantoro, D., & Handayani UN, D. (2022). Klasifikasi Penyakit Tanaman Jagung Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Ilmiah Universitas Batanghari Jambi, 22(2), 900. https://doi.org/10.33087/jiubj.v22i2.2065
Manalu, S. (2024). Dampak Perubahan Iklim Terhadap Produksi Pertanian di Indonesia. Literacy Notes, 2. https://liternot.com/index.php/ln/article/view/169
Marianah, L. (2020). Serangga Vektor dan Intensitas Penyakit Virus pada Tanaman Cabai Merah. AgriHumanis: Journal of Agriculture and Human Resource Development Studies, 1(2), 127–134. https://doi.org/10.46575/agrihumanis.v1i2.70
Muna Ramadhani, A. N., Saraswati, G. W., Agung, R. T., & Santoso, H. A. (2023). Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(4), 940–946. https://doi.org/10.29207/resti.v7i4.5028
Naura, A. (2018). Dampak Perubahan Iklim Terhadap Produksi dan Pendapatan Usaha Tani Cabai Merah (Kasus Dusun Sumberbendo, Desa Kucur, Kabupaten Malang. 2. https://doi.org/10.21776/ub.jepa.2018.002.02.8
Saputra, I., & Krisyanti, D. A. (2022). Machine Learning Untuk Pemula. Informatika Bandung.
Setyadi, R. A., Rahman, S., Manurung, D., Hasanah, M., & Indrawati, A. (2024). Implementasi Transfer Learning Untuk Klasifikasi Penyakit Pada Daun Cabai Menggunakan Cnn. Djtechno: Jurnal Teknologi Informasi, 5(2), 304–315. https://doi.org/10.46576/djtechno.v5i2.4642
Suyanto. (2022). Machine Learning: Tingkat Dasar dan Lanjut Edisi - 2 (2nd ed.). Informatika Bandung.
Vo, H. T., Ngoc, H. T., & Quach, L. Da. (2023). An Approach to Hyperparameter Tuning in Transfer Learning for Driver Drowsiness Detection Based on Bayesian Optimization and Random Search. International Journal of Advanced Computer Science and Applications, 14(4), 828–837. https://doi.org/10.14569/IJACSA.2023.0140492
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Aryawijaya, Kusnawi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
