关键词: 图像分割, 不同分类器, 简单的线性迭代聚类（SLIC）方法, Cg分量, 光照偏强

Abstract: Precision agriculture is the trend of agricultural development, while farmland image segmentation is the pre-mise and foundation of precision agriculture. Since the effect of farmland images highlights areas lost plant features on the quality of the images segmentation, based on the SLIC method and Cg component in YCrCb color space, taking use of different classifiers, this paper realizes farmland images segmentation under the condition of strong light. Firstly, farmland images are pre-processing using SLIC, to obtain super pixel region. To avoid plant pages losing green features because of the highlight areas under strong light, this paper introduces the Cg component in YCrCb color space and excess green characteristic. To avoid high requirements for the training sample in supervised learning, this paper uses semi-supervised learning, mixes up the labeled samples with samples without a label. Finally, images segmentation takes different classifiers, and the quality of image segmentation is evaluated by confusion matrix and Kappa coefficient. Contrasting the experimental results, the kernel function which is diagQuadratic in distance discriminance is better than other methods, the accuracy is higher.

Key words: image segmentation, different classifiers, Simple Linear Iterative Clusterign（SLIC） method, Cg component, strong light