Micro dataset segmentation for vanilla disease detection

Abstract

Vanilla cultivation in Indonesia is often affected by various plant diseases, which pose significant challenges to productivity. However, images of vanilla disease data available on the internet are very limited for public access; the easily accessible disease images are mostly found in books related to vanilla or vanilla gardening. In this study, data collection involved 18 images of vanilla plants infected with basal stem rot caused by the fungus Sclerotium rolfsii and 20 images of healthy green vanilla plants sourced from websites. A pretrained U-Net model from “ImageNet” with a mobile network version 2 (MobileNetV2) backbone was employed, using parameters of 100 epochs, batch size 16, image size 256 × 256, and learning rate 0.0001. The model was trained using four different color preprocessing methods: RGB, CIE Lab, HSV, and YCbCr. Experimental results showed that the YCbCr color representations consistently achieved the best segmentation performance on both training and validation datasets. Color representations such as CIE Lab and YCbCr can better capture contrast and lighting variations compared to RGB, resulting in a more robust and generalizable model.