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In the process of citrus harvesting, it is necessary to obtain information about branches and trunks of fruit trees for obstacle avoidance. In natural environment, problems such as random growth posture, different shapes and blocked branches and trunks often arise. In order to complete the acquisition of complete information of branches, the small area recognition of citrus fruit branches was completed by grid marking. The precision rate of the training model under the test set was 98.15% and the average recall rate was 81.09%, and the marker formula could still achieve better recognition results. Because the identified small areas were discrete and discontinuous, it was necessary to divide and sort the discrete areas in order to reconstruct the branches and trunks of citrus trees. At the same time, in order to solve the problems of too many background areas in Mask R-CNN model recognition frame and the recognition frame can not rotate with the growth of branches, the discrete mask obtained from Mask R-CNN model was processed with minimum external moment, and the rectangular border with minimum external moment was used to replace the recognition frame of the original model. Secondly, through the statistical analysis of the position information such as angle and distance between the centerlines of adjacent recognition frames after processing, it was found that there were constraints on the parameters such as angle and distance between centerlines. Therefore, it was proposed to use multi-parameter variable constraints to complete the division of identical recognition frames, in order to reconstruct the branches more in line with the actual growth posture of the branches and improve the ignorance. In the detection of different regions, the center point of the identical trunk recognition frame was fitted by quadratic polynomial, and the fitting error was 11.47%. Finally, the experimental results showed that the citrus tree branch reconstruction accuracy rate was 8864%. This method can provide a basis for the robot to avoid obstacles safely.