Detection of Porang Plant Diseases and Pests (Amorphophallus Muelleri) Based on Leaf Imagery Utilizing DCNN Transfer Learning
Deteksi Penyakit Dan Hama Tanaman Porang (Amorphophallus Muelleri) Berdasarkan Citra Daun Memanfaatkan DCNN Transfer Learning
Abstract
Produk olahan dari umbi tanaman porang (Amorphophallus muelleri) selalu diminati di kawasan Asia: Jepang, China, Korea, serta negara kawasan Australia. Umbi tanaman porang dapat digunakan sebagai bahan baku industri kosmetik dan juga memiliki potensi untuk mencegah berbagai penyakit manusia, karena memiliki kandungan glaukoma yang tinggi. Untuk mendapatkan umbi porang yang berkualitas baik, banyak petani menghadapi berbagai penyakit seperti penyakit busuk daun, virus mozaik (mosaik konjac) dan serangan hama pada daun tanaman porang. Dalam studi ini, diajukan sebuah arsitektur deep learning untuk klasifikasi penyakit daun pada tanaman porang. Kinerja dari model CNN Custome dibandingkan dengan model deep learning lainnya. Semua model dilatih pada kumpulan data asli dan data augmentasi dari 1000 gambar. Pendekatan Transfer Learning digunakan untuk melatih semua model deep learning. Hasil pengujian dataset menunjukkan bahwa model arsitektur EfficientNetV2M mencapai skor tertinggi dibandingkan dengan model deep learning lainnya pada dataset augmentasi dengan akurasi sebesar 98,44%. Sedang ResNet50 pada dataset asli dengan nilai 97,66% pada dataset asli, sedangkan CNN Custome dengan akurasi sebesar 89,06% dan 85,94% untuk semua dataset.
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[6] M. Sibiya, Malusi and Sumbwanyambe, “A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks,†AgriEngineering, vol. 1, pp. 119--131, 2019.
[7] X. Xie, Y. Ma, B. Liu, J. He, S. Li, and H. Wang, “A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks,†Front. Plant Sci., vol. 11, p. 751, 2020.
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Published
2023-07-19
How to Cite
ZUHAN, Miftahuz; KRISTIAN, Yosi.
Detection of Porang Plant Diseases and Pests (Amorphophallus Muelleri) Based on Leaf Imagery Utilizing DCNN Transfer Learning.
Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer, [S.l.], v. 3, n. 2, p. 129-140, july 2023.
ISSN 2776-6195.
Available at: <https://ejournal.uniska-kediri.ac.id/index.php/JTECS/article/view/3709>. Date accessed: 21 jan. 2025.
doi: https://doi.org/10.32503/jtecs.v3i2.3709.
Section
Komputer
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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