.png)
.png)
.png)
Deteksi Penyakit dan Daun Kering pada Tanaman Jagung Menggunakan YOLOV8
Abstract
Corn plants can grow well in areas with hot or tropical temperatures as long as there is adequate rainfall and an adequate irrigation system. Corn is a strategic agricultural commodity that plays an important role in the economy, both on a national and global scale. According to data from the official website satudata.pertanian.go.id, the projection of corn production in Indonesia in the period 2020 to 2024 is estimated to experience a stable annual increase, ranging from 0.94% to 0.97%. However, during its life cycle from seed to seed, every part of the corn is susceptible to a number of diseases that can reduce the quantity and quality of the results. Therefore, the problem of disease is one of the factors that constrains the production and quality of seeds. In this study, detection of types of diseases and pests in corn plants was carried out using YOLOV8 technology as a form of innovation in corn agricultural intelligence. The dataset used in this study consists of four classes of corn leaf images, namely dry spots, blight, rust and healthy plants with a total of 1162 datasets. The dataset was taken at the same time using the POVA Pro5 smartphone. Based on the results of model training and evaluation, it was obtained that with a batch size of 32 and epoch 64, the precision value reached 0.67, recall 0.78, f1 score 0.67, Map0.5 0.701, and Map0.5:0.95 0.295. Meanwhile, with a batch size of 64 and epoch 100, the precision value increased to 0.75, recall 0.79, f1 score 0.75, Map0.5 0.792, and Map0.5:0.95 0.343. These findings indicate that the application of YOLOv8 technology has the potential to provide significant contributions to the development of smart farming systems, especially in efforts to detect early disturbances in corn plants automatically and efficiently. The availability of accurate information on the types of diseases and pests that attack corn plants allows farmers to respond quickly and appropriately, including through the selection of more targeted pesticide use or the application of organic control methods that are appropriate to field conditions.
Keywords
Full Text:
PDFReferences
Sistem Pemantauan Pasar dan Kebutuhan Pokok Kementerian Perdagangan.
D. Irfansyah, M. Mustikasari, and A. Suroso, “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi
Hama Pada Citra Daun Tanaman Kopi,” Jurnal Informatika, vol. 6, no. 2, 2021.
J. G. M. Esgario, P. B. C. de Castro, L. M. Tassis, and R. A. Krohling,“An app to assist farmers in the identification of diseases and pests ofcoffee leaves using deep learning,” Information Processing in Agriculture, vol. 9, no. 1, pp. 38–47, Mar. 2022, doi: 10.1016/j.inpa.2021.01.004.
S. A. Sabrina and W. F. al Maki, “Klasifikasi Penyakit pada Tanaman Kopi Robusta Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” e-Proceeding of Engineering, vol. 9, no. 3, p. 119, Jun. 2022.
J. G. M. Esgario, R. A. Krohling, and J. A. Ventura, “Deep learning for classification and severity estimation of coffee leaf biotic stress,”Comput Electron Agric, vol. 169, Feb. 2020, doi: 10.1016/j.compag.2019.105162.
S. Suprihanto, I. Awaludin, M. Fadhil, and M. A. Z. Zulfikor,“Analisis Kinerja ResNet-50 dalam Klasifikasi Penyakit pada Daun
Kopi Robusta,” Jurnal Informatika, vol. 9, no. 2, pp. 116–122, Oct. 2022.
M. Dhanaraju, P. Chenniappan, K. Ramalingam, S. Pazhanivelan, and R. Kaliaperumal, “Smart Farming: Internet of Things (IoT)-
Based Sustainable Agriculture,” Agriculture, vol. 12, no. 10, p. 1745,2022.
V. Kakani, V. H. Nguyen, B. P. Kumar, H. Kim, and V. R. Pasupuleti, “A critical review on computer vision and artificial intelligence in food industry,” J Agric Food Res, vol. 2, p. 100033, 2020.
A. I. B. Parico and T. Ahamed, “Real time pear fruit detection and counting using YOLOv4 models and deep SORT,” Sensors, vol. 21,no. 14, p. 4803, 2021.
I. Ahmad et al., “Deep Learning Based Detector YOLOv5 for Identifying Insect Pests,” Applied Sciences, vol. 12, no. 19, p. 10167, 2022.
A. F. Bayram, C. Gurkan, A. Budak, and H. KARATA?, “A Detection and Prediction Model Based on Deep Learning Assisted by Explainable Artificial Intelligence for Kidney Diseases,” Avrupa Bilim ve Teknoloji Dergisi, no. 40, pp. 67–74, 2022.
Y. Jamtsho, P. Riyamongkol, and R. Waranusast, “Real-time Bhutanese license plate localization using YOLO,” ICT Express, vol. 6, no. 2, pp. 121–124, 2020.
D. H. Didit Iswantoro, “Klasifikasi Penyakit Tanaman Jagung Menggunakan Metode CNN,” Jurnal Ilmiah Universitas Batanghari Jambi, pp. 900-905, 2022.
R. D. A. Ivan Pratama Putra, “Klasifikasi Penyakit Daun Jagung Menggunakan Coonvolutional Neural Network,” Jurnal Algoritme, vol. 2, pp. 102-112, 2022.
R. M. A. S. Z. Afivah Dwi Nurcahyati, “Klasifikasi Citra Penyakit pada Daun Jagung Menggunakan Deep Learning dengan Metode Convolutional Neural Network,” Jurnal Ilmiah Teknologi Informasi dan Sains , vol. 1, pp. 43-51, 2022.
A. Y. Arfan Maulana, “Penerapan Metode Convolutional Neural Network Dalam Klasifikasi Penyakit Tanaman Jagung,” Seminar Nasional Teknologi dan Sains, vol. 1, pp. 251-256, 2024
A. B. Prakosa, “Implementasi Model Deep Learning Convolutional Neural Network Pada Citra Penyakit Daun Jagung Untuk Klasifikasi Penyakit Tanaman,”Jurnal Pendidikan Teknologi Informasi, vol. 6, pp. 107-116, 2023.[18] T. D. P. Z. Z. Y. Rajnapramitha Kusumastuti, “Klasifikasi Citra Penyakit Daun Jagung Menggunakan Algoritma CNN Effcientnet,” Multitek Indonesia, vol. 2,
pp. 143-153, 2024.
M. I. Fauzi, “Klasifikasi Image Tinggi Tanaman Jagung dengan Menggunakan Algoritma CNN,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 3, 2024.
R. W. R. H. Reza Mawarni, “Implementasi Metode CNN Pada Klasifikasi Penyakit Jagung,” Prosiding SEMNAS INOTEK, vol. 3, pp. 1256-1263, 2023.
M. F. N. Sayyid, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode CNN dengan Image Processing HE dan CLAHE,” Jurnal Teknik Informatika dan Teknologi Informasi, vol. 4, pp. 86-95, 2024.
Q. N. Azizah, “Klasifikasi Penyakit Daun Jagung Menggunakan Metode Convolutional Neural Network Alexnet,” Jurnal Teknik Informatika, vol. 2, pp. 28-33, 2023.
S. A. Fiviana Sulistiyana, “Aplikasi Deteksi Penyakit Tanaman Jagung Dengan Metode CNN dan SVM,” Prosiding SENATIK, vol. 6, pp. 423-432, 2024
A. R. Pratama, “Klasifikasi Citra Penyakit Daun Jagung Menggunakan CNN Arsitektur ResNet-50 dan Augmentasi Data,” Universitas Islam Negeri Sultan Syarif Kasim, 2024.
DOI: https://doi.org/10.30865/jurikom.v12i2.8504
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Aditya Dwi Putro. W, Cahyo Prihantoro, Queenta Paradissa, Wahyu Nurfida. A
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
JURIKOM (Jurnal Riset Komputer)
Dikelola oleh Universitas Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : lppm.ubd@gmail.com