Journal of Integrative Agriculture ›› 2017, Vol. 16 ›› Issue (07): 1547-1557.DOI: 10.1016/S2095-3119(16)61497-1

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  • 收稿日期:2016-09-01 出版日期:2017-07-20 发布日期:2017-07-06

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

YAO Qing1, CHEN Guo-te1, WANG Zheng1, ZHANG Chao1, YANG Bao-jun2, TANG Jian2   

  1. 1 School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
    2 State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, P.R.China
  • Received:2016-09-01 Online:2017-07-20 Published:2017-07-06
  • Contact: Correspondence TANG Jian, Tel: +86-571-63370331, E-mail: tangjian@caas.cn
  • About author:YAO Qing, Mobile: +86-13958015661, Tel: +86-571-86843324, E-mail: q-yao@zstu.edu.cn;
  • Supported by:

    This work was financially supported by the National High Technology Research and Development Program of China (863 Program, 2013AA102402), the 521 Talent Project of Zhejiang Sci-Tech University, China, and the Key Research and Development Program of Zhejiang Province, China (2015C03023).

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Abstract:     A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Traditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers (WBPHs, Sogatella furcifera (Horváth)) and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier that was trained on Gabor and Local Binary Pattern (LBP) features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

Key words: white-backed planthopper , developmental stage ,  automated detection and identification ,  image processing ,  histogram of oriented gradient features ,  gabor features ,  local binary pattern features