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The existence of axillary buds of cherry tomato growing between stem and branches will waste nutrients, resulting in a decrease in production. So they should be removed regularly. At present, they are removed manually, which increases the cost of production greatly. Using robots instead of by hands can reduce the costs. The key issue was the position of cherry tomato buds growing point detected by machine vision. An image processing method based on blue light staining was proposed. A monocular camera assisted with ultrasonic displacement sensor was used for capturing images and getting the 3D coordinate of axillary bud growing point. It was difficult to segment image, because the color of the axillary buds, branches and stems of cherry tomato was same to those of background. A blue LED light source was used to irradiate the axillary buds in order to dye the buds blue. The background was the other tomato plants whose color was green, so it was easy to extract the object from image. The image collected was complete, when the distance between the LED light source and the plant was 13cm. B component image in RGB spatial domain was a gray image and its histogram was bimodal. The gray value was selected as a threshold, and then the image was segmented, the outline of the object could be gotten clearly. However, there were burrs on the edge of the outline, so the gray image should be translated into frequencydomain diagram by fast Fourier transform (FFT). A low pass filter was used to filter out the burrs at high frequency, and the outline at low frequency was retained. The cutoff frequency was set to 28% of the maximum frequency of the image. After the inverse transformation, the burrs could be removed completely. Deformation would occur at the edge of the contour, but it did not affect the subsequent processing. The corner points at both ends of the axillary bud were key feature points. In order to highlight the characteristics of the key feature points, the morphological dilation of image was processed by the 7×7 cross structure element. Then all the corners on the image were found out by using the Shi-Tomasi corner detection algorithm. A discriminant condition was set after analyzing the growth characteristics of cherry tomato axillary buds. Then all the corners were iterated over, if there were two corners in accordance with the discriminant requirement, then the two points were the key feature points, and the midpoint of the two points was the axillary bud growth point. If there was not a couple of corners meet the requirement, then there was no axillary bud growth. If there were two couples corner points meet the discriminant requirement, it showed that there were two buds. There were errors between the axillary bud growth points located by the images and actual points. The error could be accepted since it was within 1cm. 90 images of cherry tomato plants with axillary buds growing were identified, 82 images could be detected the axillary bud successfully, the correct recognition rate was 93.94%. After the removal of axillary buds, stubble length less than 1cm accounted for 88.9%.