CornLeafNet: Disease-Area-Based Corn Leaf Disease Classification Using Convolutional Neural Networks
DOI:
https://doi.org/10.65897/jais.v2.i2.96Keywords:
CornLeafNet, corn leaf disease, disease-area-based classification, Convolutional Neural Network, ONNXAbstract
Corn leaf diseases can reduce crop productivity by disrupting photosynthesis and plant growth. Manual identification in large-scale fields remains limited due to its dependence on observer expertise, visual similarity among disease symptoms, and variations in field conditions. This study proposes CornLeafNet, a Custom Convolutional Neural Network model for disease-area-based corn leaf disease classification. The dataset consists of XML-annotated corn leaf images, from which disease-affected regions were extracted through annotation parsing and bounding box-based cropping to focus the model on symptomatic leaf areas. CornLeafNet was developed to classify three disease categories: Grey Leaf Spot, Corn Rust, and Leaf Blight. The model achieved a validation accuracy of 97.66% and a testing accuracy of 98.60%, with precision, recall, and F1-score values of 0.9860, respectively. The best-performing model was converted into ONNX format and deployed in a web-based prototype for image- and video-based classification. The testing results showed that all core system functions operated as expected, indicating that CornLeafNet has potential as an automatic and practical support model for early corn leaf disease identification.
References
[1] F. Melia, F. M. Aldian, M. S. F. Pahlevi, R. N. I. Risqullah, and S. Oktaffiani, “PERAN PEMERINTAH DALAM MENINGKATKAN VOLUME EKSPOR JAGUNG,” JURNAL ECONOMINA, vol. 2, no. 1, pp. 1305–1320, Jan. 2023, doi: 10.55681/economina.v2i1.287.
[2] P. S. Kobesi, H. Firmansyah, W. B. Devita, O. W. Matoneng, and A. Sarifuddin, “STRATEGI PENGEMBANGAN USAHATANI JAGUNG DI KABUPATEN TANAH LAUT, PROVINSI KALIMANTAN SELATAN,” ZIRAA’AH MAJALAH ILMIAH PERTANIAN, vol. 51, no. 2, p. 607, May 2026, doi: 10.31602/zmip.v51i2.22922.
[3] A. P. N. Kamal, A. Suyadi, T. Pribadi, and O. D. Hajoeningtijas, “Pengaruh Defoliasi dan Pemberian Pupuk MKP terhadap Pertumbuhan dan Hasil Jagung (Zea Mays L.),” Proceedings Series on Physical & Formal Sciences, vol. 8, pp. 36–41, Mar. 2025, doi: 10.30595/pspfs.v8i.1469.
[4] Prasetyo Mimboro, “Identifikasi Penyakit Tanaman Jagung berdasarkan Citra Daun Menggunakan Hybrid k-NN - CNN,” Journal of Cyber Health and Computer, vol. 1, no. 1, pp. 6–9, Dec. 2023, doi: 10.64163/jochac.v1i1.2.
[5] H. Hamidson and R. A. Efendi, “The Main Disease and its Attacks in the Generative Phase of Maize (Zea Mays L) in the Freshwater Swamps of South Sumatra,” Jurnal Lahan Suboptimal : Journal of Suboptimal Lands, vol. 10, no. 2, pp. 195–201, Oct. 2021, doi: 10.36706/jlso.10.2.2021.540.
[6] D. S. Sainong, R. Iswati, N. Nurdin, A. P. Solihin, S. H. Purnomo, and S. I. Pulogu, “Evaluasi Efektivitas Aplikasi Kompos Trichoderma reesei (Isolat TZ31DU1) Sebagai Agen Hayati dalam Menekan Serangan Penyakit Penting pada Tanaman Jagung,” AGROSCRIPT: Journal of Applied Agricultural Sciences, vol. 7, no. 1, pp. 109–125, Jun. 2025, doi: 10.36423/agroscript.v7i1.2244.
[7] M. Martinez‐Ramon, M. Ajith, and A. R. Kurup, Deep Learning. Wiley, 2024. doi: 10.1002/9781119861898.
[8] H. Habibi Aghdam and E. Jahani Heravi, Guide to Convolutional Neural Networks. Cham: Springer International Publishing, 2017. doi: 10.1007/978-3-319-57550-6.
[9] Badan Pusat Statistik Provinsi Nusa Tenggara Barat, “Luas Panen dan Produksi Jagung di Provinsi Nusa Tenggara Barat 2024 (Angka Tetap),” Mar. 2025. [Online]. Available: https://ntb.bps.go.id/id/pressrelease/2025/03/03/1098/luas-panen-dan-produksi-jagung-di-provinsi-nusa-tenggara-barat-2024–angka-tetap-.html
[10] Pemerintah Provinsi Nusa Tenggara Barat, “Panen Jagung Bersama, Mentan RI Optimis Gubernur NTB Mampu Jadikan Sumbawa sebagai Sentra Hilirisasi Pangan,” Apr. 2025. [Online]. Available: https://ntbprov.go.id/post/panen-jagung-bersama-mentan-ri-optimis-gubernur-ntb-mampu-jadikan-sumbawa-sebagai-sentra-hilirisasi-pangan
[11] U. Khaira, I. Weni, and W. Wilia, “Rancang Bangun Aplikasi Deteksi Penyakit Tanaman Jagung Melalui Citra Daun Berbasis Android Menggunakan Algoritma Convolutional Neural Network,” Jurnal Pepadun, vol. 5, no. 1, pp. 1–11, Apr. 2024, doi: 10.23960/pepadun.v5i1.210.
[12] Muhammad Naufal Anugrah and Nur Rachmat, “Klasifikasi Penyakit Daun Tanaman Jagung Menggunakan Pendekatan Transfer Learning Arsitektur MobileNetV4,” Jurnal Teknologi dan Manajemen Industri Terapan, vol. 4, no. 4, pp. 2174–2182, Dec. 2025, doi: 10.55826/jtmit.v4i4.1393.
[13] M. Rijal, A. M. Yani, and A. Rahman, “Deteksi Citra Daun untuk Klasifikasi Penyakit Padi menggunakan Pendekatan Deep Learning dengan Model CNN,” Jurnal Teknologi Terpadu, vol. 10, no. 1, pp. 56–62, Jul. 2024, doi: 10.54914/jtt.v10i1.1224.
[14] Kusdatul Komariyah, Ratri Enggar Pawening, and Moh Furqan, “Klasifikasi Penyakit Jagung Berdasarkan Citra Batang Menggunakan Metode Convolutional Neural Network (CNN) pada Arsitektur Mobilenet Berbasis Web,” JOKI: Journal of Computing and Informatics, vol. 2, no. 2, pp. 11–19, Dec. 2025, doi: 10.65678/joki.v2i2.206.
[15] I. Wirabowo and I. Susilawati, “Implementasi Convolution Neural Network (CNN) untuk Deteksi Penyakit pada Daun Jagung Berbasis Citra Digital,” Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer), vol. 5, no. 1, pp. 233–241, Jun. 2025, doi: 10.55382/jurnalpustakadata.v5i1.1046.
[16] H. Herfandi, R. Bin Mofidul, and I. A. Khan, “Structural Correlation Patterns in Regional COVID-19 Surveillance Data and Implications for Epidemiological Monitoring,” Jurnal Masyarakat Informatika, vol. 17, no. 1, pp. 113–124, Feb. 2026, doi: 10.14710/jmasif.17.1.77361.
[17] H. Herfandi, M. A. Aldiansyah, M. J. M. Julkarnain, and A. Susilo Yuda Irawan, “Penerapan Algoritma You Only Look Once (YOLO) Versi 8 dan Convolutional Neural Network (CNN) Dan Image Processing pada Aplikasi Automatic Number Plate Recognition (ANPR),” Syntax : Jurnal Informatika, vol. 14, no. 01, pp. 63–77, May 2025, doi: 10.35706/syji.v14i01.13074.
[18] H. Herfandi et al., “Hybrid OOK–OFDM Optical Camera Communication for Real-Time Indoor Mobility With Multi-LED Support,” IEEE Internet Things J., vol. 13, no. 5, pp. 8548–8560, Mar. 2026, doi: 10.1109/JIOT.2025.3641627.
[19] H. Herfandi, S. Dwiasnati, K. A. Baihaqi, and R. Avrizal, “Perancangan Sistem Penerimaan Siswa Baru Berbasis Web Pada Sekolah Dasar Islam Plus Baitul Maal,” Faktor Exacta, vol. 15, no. 2, Aug. 2022, doi: 10.30998/faktorexacta.v15i2.12894.
[20] H. Herfandi, U. Soleha, A. S. Y. Irawan, K. A. Baihaqi, and R. Maulana, “Implementasi Algoritma Best First Search untuk Pencarian Rute Terpendek pada Aplikasi Cerdas Pendaftaran Santri Baru,” Syntax: Jurnal Informatika, vol. 11, no. 1, pp. 63–74, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Herfandi Herfandi, Eri Sasmita Susanto, Fahri Hamdani, Jonathan Afriliansyah (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
Under this license, users are permitted to copy, distribute, display, and perform the work, and to make derivative works, provided that appropriate credit is given to the author(s) and the source, and that any derivative works are distributed under the same license.
