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Mulyana Prayoga, Yogi
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Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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DETEKSI PENYAKIT TANAMAN CABAI BERBASIS DEEP LEARNING MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK SECARA REAL TIME Mulyana Prayoga, Yogi; Wahyuni, Deasy; Elisawati
JURNAL UNITEK Vol. 18 No. 2 (2025): Juli-Desember 2025
Publisher : Sekolah Tinggi Teknologi Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/unitek.v18i2.1467

Abstract

Chili peppers are a high-value horticultural commodity in Indonesia but are vulnerable to various plant diseases such as curly leaves, gemini virus, anthracnose, wilt, whitefly infestation, and armyworms. Early detection of these diseases is essential to prevent significant yield losses. This study aims to develop a chili disease detection system using a deep learning approach with a Convolutional Neural Network (CNN) architecture, specifically employing the MobileNet model, which is known for its efficiency in image classification tasks. The system is designed to operate in real-time using a device camera. Development follows the Waterfall model of the Software Development Life Cycle (SDLC), encompassing planning, analysis, design, implementation, and testing phases. Testing results indicate that the system achieves high accuracy in distinguishing between healthy and diseased chili leaves. This system is expected to assist farmers in early detection and prompt preventive actions, ultimately supporting increased productivity in chili cultivation.
Co-Authors Elisawati Wahyuni, Deasy