Deep Learning-Based Detection of Potato Leaf Diseases Using ResNet-50 with Mobile Application Deployment

Cahyono Budy Santoso; Rufman Iman Akbar  Effendi; Johannes Hamonangan  Siregar

doi:10.52436/1.jutif.2026.7.1.5186

Authors

Cahyono Budy Santoso Information System, Faculty of Technology and Design, Universitas Pembangunan Jaya, Indonesia
Rufman Iman Akbar Effendi Information System, Faculty of Technology and Design, Universitas Pembangunan Jaya, Indonesia
Johannes Hamonangan Siregar Information System, Faculty of Technology and Design, Universitas Pembangunan Jaya, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.1.5186

Keywords:

Deep learning, Potato leaf disease, ResNet-50, Mobile application, Image classification, Precision agriculture

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.

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