Effective real-time TinyML-based system for early detection and blister quantification of grape leaf blister mite (Eriophyes vitis (Pagst.)) damage

Aim of study: To develop a real-time detection system for grape leaf blister mite (Eriophyes vitis (Pagst.)), a significant pest in vineyards, using deep learning models. Early detection of pests and diseases is essential to mitigate agricultural losses, especially considering challenges such as climate change, shrinking agricultural areas, and increasing food demand. Area of study: The study was conducted in vineyard areas located in Tokat, Türkiye, a region known for its extensive viticulture. Material and methods: An Arduino Tiny ML kit and the Edge Impulse platform were utilized to deploy the FOMO (Faster Objects, More Objects) MobileNetV2 0.1 model. The model was trained using a custom dataset of grape leaf blister mite images and validated against expert observations. Real-time detection of blister counts caused by E. vitis was evaluated through comparative metrics. Main results: The trained model achieved a classification success of 90% with an F1 score on the validation dataset and an accuracy of 96.25% on the test dataset. Blister numbers were assessed using both expert observations and the real-time detection system, resulting in RMSE (Root Mean Squared Error) of 1.862 and a MAPE (Mean Absolute Percentage Error) of 15.63%. These results demonstrate the system’s reliability in detecting vineyard pests with high precision. Conclusions: The proposed system provides a practical, cost-effective, and accurate approach for pest detection in vineyards, offering significant potential to enhance pest management strategies and reduce economic losses in agriculture.