Research on apple size grading based on LabVIEW and yolo algorithm

Machine vision technology was applied to combine LabVIEW and the YOLO algorithm to detect the maximum transverse diameters of apples for apple size grading. The NI Vision module was employed for visual image processing, including grayscale and threshold segmentation and morphological processing. The image size was adjusted, and the LabelImg image annotation tool was applied to construct an apple dataset. YOLO v8 algorithm was used to train and test the developed dataset. Based on the results of the linear fitting line between the pixel values of the maximum transverse diameter of apples and the measured values of apple diameters, a visual size grading standard was established for apples, and an apple visual size grading system was designed based on LabVIEW 2019. The results of this study show that the accuracy of size detection for individual apples remained above 97.4% under different light and background conditions. This indicates that the system not only has strong feasibility but also demonstrates excellent robustness and environmental adaptability. This design was feasible and provided a research foundation for the automatic grade sorting of apples and other fruits.