<![CDATA[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.]]>
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