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All Journal JSAI (Journal Scientific and Applied Informatics) Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma Journal of Elektronik Sistem InformasI
Budy Santoso, Cahyono
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Journal : Jurnal Teknik Informatika (JUTIF)

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Deep Learning-Based Detection of Potato Leaf Diseases Using ResNet-50 with Mobile Application Deployment Budy Santoso, Cahyono; Effendi, Rufman Iman Akbar; Siregar, Johannes Hamonangan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5186

Abstract

Plant diseases significantly reduce agricultural productivity, especially in developing regions with limited access to early detection tools. This research presents a deep learning-based approach for detecting potato leaf diseases, focusing on Early blight, Late blight, and healthy conditions. A modified ResNet-50 architecture was employed and trained using a publicly available potato leaf image dataset. Preprocessing steps included data augmentation and normalization to enhance model generalization. The model achieved a high accuracy of 99.31%, with precision, recall, and F1-score all exceeding 99%, indicating excellent classification performance. This study introduces a novel approach that improves classification performance through an optimized deep learning architecture, achieving higher accuracy compared to existing models. In addition to enhancing predictive capability, the study also addresses the practical need for accessibility by integrating the trained model into an Android-based mobile application. The application allows users to upload or capture leaf images and receive real-time predictions. The interface was designed for simplicity and usability in field conditions, making it accessible to farmers and agricultural workers. The findings demonstrate that combining deep learning with mobile technology can offer an effective and scalable solution for early disease detection in agriculture. Future work may explore cross-crop adaptability and lightweight model optimization for real-time performance on low-resource devices.
Co-Authors Ahmad, Alifio Fikra Ahmad Asmaidin, Asmaidin Bhagawanta, Bajra Effendi, Rufman Iman Akbar Faizul Anwar Ramdhani Ghaniy, Rajib Hafid Roihan Harry Vadilan Sianturi Johannes Hamonangan Siregar Muhammad Mujiburochman Rachma, Mutia Raihan Cikal Herlambang Reyner Shaquille Rachim Vistiyawati, Vanessa