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All Journal Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Engineering and Technological Sciences
Herdiana, Dina
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Public Health

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Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8 Chitraningrum, Nidya; Banowati, Lies; Herdiana, Dina; Mulyati, Budi; Sakti, Indra; Fudholi, Ahmad; Saputra, Huzair; Farishi, Salman; Muchtar, Kahlil; Andria, Agus
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.1.5

Abstract

Corn is one of the primary carbohydrate-rich food commodities in Southeast Asian countries, among which Indonesia. Corn production is highly dependent on the health of the corn plant. Infected plants will decrease corn plant productivity. Usually, corn farmers use conventional methods to control diseases in corn plants. Still, these methods are not effective and efficient because they require a long time and a lot of human labor. Deep learning-based plant disease detection has recently been used for early disease detection in agriculture. In this work, we used convolutional neural network algorithms, namely YOLO-v5 and YOLO-v8, to detect infected corn leaves in the public data set called ‘Corn Leaf Infection Data set’ from the Kaggle repository. We compared the mean average precision (mAP) of mAP 50 and mAP 50-95 between YOLO-v5 and YOLO-v8. YOLO-v8 showed better accuracy at an mAP 50 of 0.965 and an mAP 50-95 of 0.727. YOLO-v8 also showed a higher detection number of 12 detections than YOLO-v5 at 11 detections. Both YOLO algorithms required about 2.49 to 3.75 hours to detect the infected corn leaves. This all-trained model could be an effective solution for early disease detection in future corn plantations.
PENGABDIAN KEPADA MASYARAKAT (PKM) PENERAPAN PENERANGAN TENAGA SOLAR CELL UNTUK MENINGKATKAN KEAMANAN DAN KESEJAHTERAAN MASYARAKAT DESA NGAMPRAH Herdiana, Dina; Puspita, Heni; Muhyiddin, Muhammad Ali; Kurniawan, Aldi; Firdaus, Muhammad Firhan; L, Yudha Budi; Fajarianto, Reyhan; Wulandari, Putri; G, Sulastri Lumban; Vincensius, Arnold; W, Samuel Putra
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 6 (2024): Vol. 5 No. 6 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i6.41689

Abstract

Mitra program Pengabdian Kepada Masyarakat (PKM) Desa Binaan ini adalah Desa Ngamprah yang terletak di Kecamatan Ngamprah, Kabupaten Bandung Barat, Provinsi Jawa Barat. Penerangan pada Desa ini belum optimal, terkhusus di RT 02, RW 05, yang memisahkan dua pemukiman warga dengan persawahan dan jurang yang curam.Oleh karena di wilayah tersebut sangat gelap, sering terjadi kecelakaan dan hal-hal yang tidak diinginkan. Solusi untuk mengatasi permasalahan pada mitra yaitu dengan membangun infrastruktur berupa lampu jalan. Pembangunan infrastruktur ini menggunakan lampu solar cell dengan sensor gerak untuk mewujudkan penerangan yang layak untuk masyarakat agar meningkatkan keamanan dan kenyamanan. Langkah-langkah yang dilakukan dalam pencapaian tujuan PKM Desa Binaan ini dimulai dengan melakukan analisis situasi dan mengidentifikasi permasalahan, merancang bentuk lampu penerangan, pemasangan lampu penerangan, sosialisasi lampu solar cell. Luaran PKM Desa Binaan ini adalah video kegiatan, dan publikasi kegiatan di media online.
Co-Authors Ahmad Fudholi Andria, Agus Banowati, Lies Budi Mulyati, Budi Chitraningrum, Nidya Fajarianto, Reyhan Farishi, Salman Firdaus, Muhammad Firhan G, Sulastri Lumban Indra Sakti Kurniawan, Aldi L, Yudha Budi Muchtar, Kahlil Muhyiddin, Muhammad Ali Puspita, Heni PUTRI WULANDARI Saputra, Huzair Vincensius, Arnold W, Samuel Putra