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Different turfs have different growth characteristics, engendering differences in the number of maintenance cycles and amounts of pesticides used; therefore, studying their subtle color and shape differences through image recognition is crucial. Our study proposes an improved least squares support vector machine (LS-SVM) pixel classification method for this purpose. The sensitivity to local color changes in the hue, saturation, and value color space is considered, and the Sobel operator is used to extract the homogeneity as pixellevel color features. The maximum local energy, gradient, and second-order moment matrix of image pixels are obtained as texture features using a Gabor filter. Seven shape features of different plant leaves are calculated, multiple extracted features are used as LS-SVM classifier inputs, and samples are selected and trained with a dynamic threshold. The trained classifier can be used for segmentation. The experiments showed that it could use the local information of the color images and the excellent generalization ability of LS-SVM to segment lawn plants effectively. Under different weather conditions, the penalty coefficient, C, and kernel parameters with optimal generalization were Bayesian optimized to obtain a segmentation rate exceeding 95%. This algorithm yields a higher classification rate for plants with less obvious differences in texture and shape and optimizes space and time complexities.

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