Grape crops are a great source of income for farmers. The yield and quality of grapes can be improved by preventing and treating diseases. The farmer's yield will be dramatically impacted if diseases are found on grape leaves. Automatic detection can reduce the chances of leaf diseases affecting other healthy plants. Several studies have been conducted to detect grape leaf diseases, but most fail to engage with end users and integrate the model with real-time mobile applications. This study developed a mobile-based grape leaf disease detection (GLDD) application to identify infected leaves, Grape Guard, based on a TensorFlow Lite (TFLite) model generated from the You Only Look Once (YOLO)v8 model. A public grape leaf disease dataset containing four classes was used to train the model. The results of this study were relied on the YOLO architecture, specifically YOLOv5 and YOLOv8. After extensive experiments with different image sizes, YOLOv8 performed better than YOLOv5. YOLOv8 achieved 99.9 ​% precision, 100 ​% recall, 99.5 ​% mean average precision (mAP), and 88 ​% mAP50–95 for all classes to detect grape leaf diseases. The Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.