PENERAPAN METODE CNN RESNET152 PADA PENGEMBANGAN APLIKASI VANILLATECH BERBASIS MOBILE UNTUK IDENTIFIKASI PENYAKIT TANAMAN VANILI - Mush'ab Al Mubarak; Lilis Nur Hayati; St Hajrah Mansyur (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Vanilla is a high-value plantation commodity whose productivity is significantly affected by plant diseases that are difficult to identify accurately using conventional methods. This study aims to develop a mobile-based vanilla plant disease identification system using a Convolutional Neural Network (CNN) with the ResNet152 architecture. The dataset consists of primary field-acquired images, which were augmented to produce a total of 1,616 images across five disease classes. The model was trained using a transfer learning scheme with parameter adjustments designed to handle variations in field lighting conditions, image angles, and real-world visual characteristics. Experimental results demonstrate that the proposed ResNet152 model achieves high and stable classification accuracy. The integration of the trained model into a mobile application enables fast and practical disease diagnosis in real plantation environments. The novelty of this study lies in the field-oriented optimization of the ResNet152 model and its direct deployment in a mobile diagnostic system tailored for vanilla plant disease identification.