Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases

Shanwen Zhang, Yihai Zhu, Zhuhong You, Xiaowei Wu

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52 引用 (Scopus)

摘要

Cucumber diseases can be detected and recognized automatically based on diseased leaf symptoms. In this paper, we propose a new method, combining superpixels, expectation maximization (EM) algorithm, and logarithmic frequency pyramid of histograms of orientation gradients (PHOG), to recognize cucumber diseases. The proposed method is composed of following steps. First, the superpixel operation is used to divide a diseased leaf image into a number of compact regions, which can dramatically accelerate the convergence speed of the EM algorithm that is adopted to segment the diseased leaf regions and obtain the lesion image. Second, the logarithmic frequency PHOG features are extracted from the segmented lesion image. Finally, Support Vector Machines (SVMs) are performed to classify and recognize different cucumber diseases. Conducted on a database of cucumber diseased leaf images, experimental results show the proposed method is effective and feasible for recognizing cucumber diseases.

源语言英语
页(从-至)338-347
页数10
期刊Computers and Electronics in Agriculture
140
DOI
出版状态已出版 - 8月 2017
已对外发布

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