Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

The segmentation of broccoli seedlings in the crops and weeds co-exist field environment is of great significance for weeding and herbicide spraying. This paper constructed a crop segmentation algorithm with a small training set for discriminating broccoli seedlings from weeds and soil. This algorithm was based on a support vector machine (SVM) combined with color-texture features. Correlation analysis and chi-square tests were used to select 6 features from the 21 color features. Gray-level co-occurrence matrix (GLCM) was used to extract 5 texture features. And each parameter of GLCM had been assessed and optimized by the chi-square test. Linear Discriminant Analysis (LDA) was used to decompose the original dataset in a set of 3 successive orthogonal components. This method selected features more reasonable and gained higher plant segmentation accuracy. When the training sample is greater than 50, the accuracy of the test set could reach 90%. The coefficient of determination ( $R^{2}$ ) between the ground truth broccoli seedling area and the segmentation broccoli area was 0.91, and the root-mean-square error ( $\sigma $ ) was 0.10. Results demonstrated that the color-texture features were able to effectively segment broccoli seedlings even when there was a significant amount of weeds.