Smartphone-based Tool for Two-Spotted Spider Mite Detection in Strawberry

Abstract

<b><sc>Abstract.</sc></b> <b>Two-spotted spider mite (TSSM) is one of the most harmful pests for the strawberry plant. TSSM usually feeds on strawberry leaf and affect photosynthesis. Counting TSSM (larva, protonymph, deutonymph, and adult) and TSSM eggs can help growers apply the right amount of pesticide or release the right amount of predatory mite. However, the traditional manual counting method is time-consuming. For the past several years, many deep learning-based object detection models achieved state-of-the-art performance, including Faster R-CNN, YOLO, and SSD. These methods outperformed the traditional computer vision and machine learning methods. Therefore, this study adopted a deep learning-based object detection method for TSSM and TSSM eggs counting and developed an Android app for growers to use in the field. In this study, 875 images were collected using a smartphone with a macro lens. Then, YOLO was trained to detect TSSM and TSSM eggs, and the trained YOLO model was deployed in the Android App. The mean average precision of the model was 0.645, and the average precision for TSSM and TSSM eggs was 0.62 and 0.67, respectively. This study showed that a deep learning-based smartphone app could effectively detect TSSM and TSSM eggs from the image. When more images are collected, and data augmentation techniques are used in model training, the mite detection accuracy could be much improved. </b>