Restricted by imaging equipment and external natural environment, apple image produces lots of noise in the process of collection and transmission, which is one of the important factors that affect the accuracy and efficiency of image recognition. In order to reduce the effect of the noise on the target identification of apple harvesting robot, the segmentation method for apple image with noise is studied, which is not affected by noise. Firstly, by constructing similarity matrix, gray value, local spatial information and non-local spatial information of each pixel are utilized to construct a three-dimensional feature dataset. And then, the space compactness function is introduced to compute the similarity between each feature point and its nearest neighbors. Obviously, the similarity matrix is sparse matrix. Secondly, the outliers of similarity matrix are tuned by splitting the outlier matrix and representing it linearly with the other remaining column vector. Finally, tuned similarity matrix is decomposed by Laplacian vector, and eigenvector matrix is constructed and then normalized; the next step is that row vector of the matrix is clustered by k-means algorithm. The clustering result is obtained for three-dimensional feature dataset, and the image segmentation result is also obtained. The experiments of 2 apple images are carried out to validate the optimization algorithm proposed in the paper. The segmentation accuracy of the optimization method for a single apple under the influence of different noise is over 99%. The segmentation accuracy is over 98% for overlapping apple. The segmentation accuracy rate is 99.014% on average for 30 apple images, which is under the influence of Gaussian noise with the variance of 0.05 and salt and pepper noise with the probability of 0.01. The results of optimization method are compared with the results of the original spectral clustering algorithm and the spectral clustering algorithm based on space feature. The advantage of the optimization method is achieving de-noising effect. Also, the role of tuning the similar matrix’s outliers is to achieve clustering optimization. In the setting conditions of this experiment, the segmentation accurate rate can be improved by 5%-6% compared to the spectral clustering algorithm based on space feature, and by 9%-25% compared to the original spectral clustering algorithm. At the end, the running time is analyzed and compared for the algorithms, and the experiments of 30 images, which contain 3 types of images i.e. 128×128, 256×256 and 512×512 pixels and each type has 10 images, are carried out to validate the algorithm’s efficiency. From the result of experiments, we know the optimization algorithm’s running time is less than the original spectral clustering algorithm and is close to the spectral clustering algorithm based on space feature on the premise of achieving better segmentation accurate rate. Through the analysis and comparison, the conclusions obtained from the study are as follows: first, the optimization algorithm has the robustness for the noise; second, the optimization algorithm reduces the wrong rate of the boundary region’s pixels; third, the optimization algorithm improves the segmentation accuracy and efficiency. The results provide a reference for fast target recognition of apple harvesting robot. [ABSTRACT FROM AUTHOR]