Article Per Year (5 Year)
p-Index From 2020 - 2025
2.33
P-Index
This Author published in this journals
All Journal Biota: Jurnal Ilmiah Ilmu-Ilmu Hayati JURNAL HAMA DAN PENYAKIT TUMBUHAN TROPIKA Biosaintifika: Journal of Biology & Biology Education JURNAL ILMIAH AGRINECA Jurnal Litbang Provinsi Jawa Tengah Agricultural Science GANESHA: Jurnal Pengabdian Masyarakat PROFICIO: Jurnal Pengabdian Masyarakat Journal of Applied Agricultural Science and Technology Journal of Community Capacity Empowerment Biosaintifika: Journal of Biology & Biology Education
Dwi Susilo Utami
Universitas Tunas Pembangunan Surakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Materials Science & Nanotechnology Public Health Social Sciences Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Applied Agricultural Science and Technology

 1 
The Analysis of Architectural YOLOv5 Convolutional Neural Networks for Detecting Apple Leaf Diseases Erkamim, Moh.; Subarkah, Muhammad Zidni; Soelistijono, R.
Journal of Applied Agricultural Science and Technology Vol. 9 No. 1 (2025): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v9i1.251

Abstract

Apple cultivation is crucial to agricultural economies, particularly in regions with sub-tropical climates, such as Indonesia, where apple farming is expanding rapidly. However, managing diseases and pests is essential for maintaining optimal crop yields, as they can significantly reduce production. Among the common diseases affecting apple trees are Scab, Black Rot, and Cedar Apple Rust, which primarily impact leaves and threaten the total health of the plant. Therefore, this research aimed to develop an effective model for detecting apple leaf diseases using the architectural YOLOv5 Convolutional Neural Networks (CNNs). The analysis was conducted between November 2022 and February 2023 at the Smart City Information System (SIKC) laboratory, including 120 apple leaf samples collected from Tawangmangu. Additionally, secondary data containing 30 images for each disease category, consisting of Healthy, Scab, Black Rot, and Cedar Apple Rust, were used as a benchmark. The performance of YOLOv5 was evaluated based on several metrics, including Precision, Recall, mAP@0.5, and mAP@0.5:0.95. The results showed that Cedar Apple Rust was the most prevalent disease identified among the samples. YOLOv5 performed exceptionally well in detecting disease symptoms, achieving a Precision score of 0.810, Recall of 0.981, mAP@0.5 of 0.950, and mAP@0.5:0.95 of 0.765 on the test dataset. These results showed that the proposed model was highly accurate and reliable for the early detection of apple leaf diseases, offering significant potential for improving disease management strategies and increasing the efficiency of apple production.
Co-Authors Achmad Fatchul Aziez Agus Budiyono Alanusa, Awang Arif Rohman Hakim Azies, A.F. Daryanti DARYANTI DARYANTI Daryanti, B. Dian Rakhmawati, Dian Dwi Susilo Utami Endang Suprapti HARTOYO, EKO Haryuni Haryuni , Haryuni Haryuni, H. Imani Fatihah, Syalaysa Joko Sulistyono KODRAT BUDIYONO Luncana Faridhoh Sasmito Mardhika S., Siti Mardhika Sari, Siti Moh. Erkamim Muhamad Zumri Nugroho, Benny Prasetio, Suji Tegar Priyadi, Sapto Priyadi Restu Putra Abadi, Pramudika Rianto, Prisma Aditya Rosa Suryantini Sapto Priyadi Sari M Setie Harieni Setie Herieni Shodiq, Yusuf Nur Sholkhan, Ahmad Siti Mardhika Sari Soemarah KD, Tyas Soemarah Kurnia Dewi, Tyas Subarkah, Muhammad Zidni Sukamto T Supriyadi T.S. Kurnia Dewi Teguh Supriyadi Tiara Maharani, Annissa Totong Umar Utomo, Herry WAHYU DWIYANTO Wibowo, Maulana Tulus Wiyono wiyono