Abstract: In order to improve the low accuracy of the traditional image segmentation method for segmenting grape cluster images in complex field environments, a grape cluster in field image segmentation method based on improved red-green difference method and Otsu algorithm is proposed. This paper selects the RGB color space which is similar to human vision, extracts and analyzes the histogram of the R and G feature maps, analyzes the point multiplication feature maps and performs Otsu operation to realize the segmentation of the red grape cluster image in the field environment. Comparing with the results of the grayscale map,（R-G） feature map and（R-G）/（R+G） feature map using the maximum threshold segmentation method（Otsu） segmentation. The experimental results show that the red and green handicap is multiplied by the Otsu segmentation method. The result which accuracy is 92.37%, and the F₁-score is 90.13% is the best, and then the morphological processing of the segmentation results can achieve a more complete segmentation of grape clusters. 50 images were tested, of which the highest accuracy was 97%, the lowest was 79%, and the average accuracy was 88.75%. The method proposed in this paper can provide a research basis for the recognition and positioning of grape clusters.