Image detection algorithm for cutting surface roughness of grape hard branch grafting based on light-section method

Abstract: In order to detect cutting surface roughness of grape grafting hard branch, this paper built a detection system of cutting surface roughness and designed an image detection algorithm of feature extraction based on measurement principle of light-section method. The detection system was made up of 9J light-section microscope, industrial camera and computer. Sampling length was an important role in the calculation of roughness. In order to make up the shortage of single sampling length (1 420 μm), this paper applied image mosaic technology to images acquired by multiple sampling, and thus got a longer sampling length. Meanwhile, a method was proposed to product matching template automatically, which could avoid the manual operation and improve the efficiency of image mosaic when matching the reference image and target image based on gray level information. Then the image mosaic algorithm was tested and the results showed that, the average running time was increased by 1.104 s and the average sampling length was increased by 1 131.77 μm when the number of mosaic image increased by a roughness feature image. Moreover, fuzzy set theory was applied to the process of gray-scale transformation and could effectively guarantee the integrity of the single edge of the image after segmentation. In this paper, Otsu algorithm was used to segment image. In order to filter the defeat profile due to catheter lumen and tracheid cavity contained by cutting surface itself, local pixels of the roughness feature binary image were operated by way of human-computer interaction. The pixels of the defect position in the foreground image were set to zero and not involved in the subsequent roughness calculation. A method was also proposed to extract the single side edge by scanning pixels of per column one by one, the first pixel whose value was not zero at each column belonged to edge pixels set. According to the measurement principle of light-section method, the corresponding position relation could be established between the single edge extracted from roughness feature image and roughness profile in one place of grape cutting surface. So the value of roughness height parameters Ra and Rz could be obtained by calculating the single edge. In order to verify the feasibility of the proposed method, the comparative experiment was conducted between the detection system built and KEYENCE VK-200 laser microscope. The experimental results showed that the relative error of Ra measured by proposed method was 6.73%, which was within the allowable range of measurement error, the image detection algorithm based on light-section method has good feasibility when applied to measure cutting surface roughness of grape hard branch. The study provides technical support for the further research of the impact of cutting parameters on cutting surface roughness and grafting survival rate of grape hard branch.