Fruit classification and quality detection system based on improved YOLOv8 model

With the expansion of fruit planting scale, the inefficiency of manual labor has gradually become a bottleneck limiting the development of the industry. In recent years, with the advancement of technology, research into modern intelligent agricultural technologies has also become a research hotspot. This paper is based on the YOLOv8 model for fruit classification and quality detection. First, image data enhancement is performed using operations such as power law transformation, and random operations like translation, flipping, and scaling are applied to augment the image dataset. A dataset is then established. Next, EfficientViT is introduced to replace the backbone network, and the SA attention mechanism is incorporated into the detection head, making the model more accurate in fruit classification and quality detection. Finally, a GUI (Graphical User Interface) visualization design is implemented to achieve a user-friendly interaction and is applied in the overall system design.