Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (2): 563-570.DOI: 10.11772/j.issn.1001-9081.2020050645

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles     Next Articles

Citrus disease and insect pest area segmentation based on superpixel fast fuzzy C-means clustering and support vector machine

YUAN Qianqian, DENG Hongmin, WANG Xiaohang   

  1. College of Electronics and Information Engineering, Sichuan University, Chengdu Sichuan 610065, China
  • Received:2020-05-15 Revised:2020-07-21 Online:2021-02-10 Published:2020-08-14


袁芊芊, 邓洪敏, 王晓航   

  1. 四川大学 电子信息学院, 成都 610065
  • 通讯作者: 邓洪敏
  • 作者简介:袁芊芊(1995-),女,四川遂宁人,硕士研究生,主要研究方向:数字图像处理、神经网络;邓洪敏(1969-),女,四川广汉人,副教授,博士,主要研究方向:人工智能、模糊控制、非线性动力学;王晓航(1996-),男,陕西渭南人,硕士研究生,主要研究方向:数字图像处理、深度学习。

Abstract: Focused on the existing problems that there are few image datasets of citrus diseases and insect pests, the targets of diseases and pests are complex and scattered, and are difficult to realize automatic location and segmentation, a segmentation method of agricultural citrus disease and pest areas based on Superpixel Fast Fuzzy C-means Clustering (SFFCM) and Support Vector Machine (SVM) was proposed. This method made full use of the advantages of SFFCM algorithm, which was fast and robust, and integrated the characteristics of spatial information, meanwhile, it did not require manual selection of samples in image segmentation like the traditional SVM. Firstly, the improved SFFCM segmentation algorithm was used to pre-segment the image to be segmented to obtain the foreground and background regions. Then, the erosion and dilation operations in morphology were used to narrow these two areas, and the training samples were automatically selected for SVM model training. Finally, the trained SVM classifier was used to segment the entire image. Experimental results show that compared with the following three methods:Fast and Robust Fuzzy C-means Clustering (FRFCM), the original SFFCM and Edge Guidance Network (EGNet), the proposed method has the average recall of 0.937 1, average precision of 0.941 8 and the average accuracy of 0.930 3, all of which are better than those of the comparison methods.

Key words: image segmentation, Fuzzy C-means Clustering (FCM), Support Vector Machine (SVM), citrus disease and insect pest

摘要: 针对目前柑橘病虫害图像数据集较少,病虫害目标复杂、散漫,难以自动定位分割的问题,提出了一种基于超像素快速模糊C均值聚类(SFFCM)与支持向量机(SVM)的农业柑橘病虫害区域分割方法。该方法充分利用了SFFCM快速、鲁棒的优点,且融合了空间信息的特点,同时避免了传统SVM在图像分割上需要人工选择样本的缺点。首先,利用改进的SFFCM分割算法对待分割图像进行预分割,得到前景和背景区域;接着利用形态学中的腐蚀和膨胀操作对前景和背景区域进行缩小,然后自动选取训练样本进行SVM模型训练;最后用训练好的SVM分类器完成整幅图像的分割。将所提方法与快速鲁棒模糊C均值聚类(FRFCM)、原始SFFCM及边缘引导网络(EGNet)这三种方法进行实验对比,结果表明所提方法的平均召回率为0.937 1,平均精确率为0.941 8,平均准确率为0.930 3,均明显优于对比算法。

关键词: 图像分割, 模糊C均值聚类, 支持向量机, 柑橘病虫害

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