您好，欢迎访问安徽省农业科学院 机构知识库！

SLIC_SVM based leaf diseases saliency map extraction of tea plant

文献类型： 外文期刊

作者： Sun, Yunyun 1 ; Jiang, Zhaohui 1 ; Zhang, Liping 2 ; Dong, Wei 2 ; Rao, Yuan 1 ;

作者机构： 1.Anhui Agr Univ, Sch Informat & Comp Sci, Hefei 230036, Anhui, Peoples R China

2.Anhui Acad Agr Sci, Inst Agr Econ & Informat, Hefei 230036, Anhui, Peoples R China

关键词： Tea plant; Leaf disease; SLIC; SVM; Saliency map

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2019 年 157 卷

页码：

收录情况： SCI

摘要： For the purpose of improving the extraction of tea plant leaf disease saliency map under complex backgrounds, a new algorithm combining SLIC (Simple Linear Iterative Cluster) with SVM (Support Vector Machine) is proposed in this paper. Firstly, super-pixel block is obtained by SLIC algorithm, significant point is detected by Harris algorithm, and fuzzy salient region contour is extracted by employing convex hull method. Secondly, the four-dimensional texture features of super-pixel blocks in salient regions and background areas are extracted, and then the classification map is obtained by classifying the super-pixel blocks with the help of SVM classifier. Lastly, the morphological and algebraic operations are implemented for repairing classified super-pixel blocks. As a result, one accurate saliency map of tea plant leaf disease image is obtained. Through testing based on 261 diseased images, the quality evaluation index, the accuracy, precision, recall and F-value are 98.5%, 96.8%, 98.6% and 97.7%, respectively. It demonstrates that the proposed method performs better than the other three SLIC-based algorithms in visual effects and quality assessment index. Such conclusion can be drawn that the proposed method can effectively extract tea plant leaf disease saliency map from complex background. Consequently, this research is expected to lay a good basis for the study of tea plant leaf disease identification. Last but not the least, the proposed method has good potential that extracts saliency map of crops or plants disease.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：安徽省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台