Designing a Mobile Application for Identifying Strawberry Diseases with YOLOv8 Model Integration

Abstract

The progress in computer vision has led to the development of potential solutions, becoming a versatile technological key to addressing challenging issues in agriculture. These solutions aim to enhance the quality of agricultural products, boost the economy's competitiveness, and reduce labor and costs. Specifically, the detection of diseases in various fruits before harvest to avoid reducing product quality and quantity still relies on the experience of long-time farmers. This leads to difficulties in controlling disease sources over large cultivated areas, resulting in uneven quality control after harvest, which may lead to low prices or failure to meet export requirements to developed markets. Therefore, this stage has now been applied with modern technology to gradually replace humans. In this paper, we propose a mobile application to detect four common diseases in strawberry trees by using image processing technology that combines an artificial intelligence network in identification: based on size, color, and shape defects on the surface of the fruit. The proposed model consists of different versions of YOLOv8 with RGB input to accurately detect diseases in strawberries and provide assessments. Among these, the YOLOv8n model utilizes the fewest parameters with only 11M, but it produces more output parameters with higher accuracy compared to some other YOLOv8 models, achieving an average accuracy of approximately 87.9%. Therefore, the proposed method emerges as one of the possible solutions for strawberry disease detection.