基于SVM和形态学的作物病斑图像分割方法

Crop Spot Image Segmentation based on SVM and Morphology

作物叶片病斑图像分割是作物病害自动识别的一个重要步骤,为了提高传统的基于阈值或聚类的叶片病斑分割方法的分割效果,提出了一种基于支持向量机(SVM)和形态学的病斑分割方法。首先利用SVM进行病斑图像分割,再利用开运算和闭运算来消除病斑图像中边缘的不连续性、病斑内部的小噪声和小洞。最后,通过对黄瓜细菌性角斑病图像进行试验,结果表明,所提出分割方法具有较好的分割效果。

Crop leaf spot image segmentation is the important steps in crop disease automatic recognition. To im-prove the leaf spot segmentation performance of the traditional threshold or clustering methods, a spot segmenta-tion based on SVM and morphology was proposed in the paper. The spot image segmentation was formulatedby SVM.The discontinuity edge, small noise, small hole and the small hole inside the lesion image were eliminated by the op-ening and closing algorithms of morphology. The experimental results showed that this approach outperformed othermethods and was effective for cucumber leaf disease segmentation.