Journal of Integrative Agriculture ›› 2014, Vol. 13 ›› Issue (8): 1736-1745.DOI: 10.1016/S2095-3119(14)60799-1

• 论文 • 上一篇    下一篇

Automated Counting of Rice Planthoppers in Paddy Fields Based on Image Processing

 YAO Qing, XIAN Ding-xiang, LIU Qing-jie, YANG Bao-jun, DIAO Guang-qiang , TANG Jian   

  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
  • 收稿日期:2014-03-26 出版日期:2014-08-01 发布日期:2014-08-02
  • 通讯作者: YAO Qing, Tel: +86-571-86843324, E-mail: q-yao@zstu.edu.cn; TANG Jian, Tel: +86-571-63370331, E-mail: tangjian@caas.cn
  • 基金资助:

    the support of the National Natural Science Foundation of China (31071678), the National High Technology Research and Development Program of China (863 Program, 2013AA102402) and Zhejiang Provincial Natural Science Foundation of China (LY13C140009).

Automated Counting of Rice Planthoppers in Paddy Fields Based on Image Processing

 YAO Qing, XIAN Ding-xiang, LIU Qing-jie, YANG Bao-jun, DIAO Guang-qiang , TANG Jian   

  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:2014-03-26 Online:2014-08-01 Published:2014-08-02
  • Contact: YAO Qing, Tel: +86-571-86843324, E-mail: q-yao@zstu.edu.cn; TANG Jian, Tel: +86-571-63370331, E-mail: tangjian@caas.cn
  • Supported by:

    the support of the National Natural Science Foundation of China (31071678), the National High Technology Research and Development Program of China (863 Program, 2013AA102402) and Zhejiang Provincial Natural Science Foundation of China (LY13C140009).

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摘要： A quantitative survey of rice planthoppers in paddy fields is important to assess the population density and make forecasting decisions. Manual rice planthopper survey methods in paddy fields are time-consuming, fatiguing and tedious. This paper describes a handheld device for easily capturing planthopper images on rice stems and an automatic method for counting rice planthoppers based on image processing. The handheld device consists of a digital camera with WiFi, a smartphone and an extrendable pole. The surveyor can use the smartphone to control the camera, which is fixed on the front of the pole by WiFi, and to photograph planthoppers on rice stems. For the counting of planthoppers on rice stems, we adopt three layers of detection that involve the following: (a) the first layer of detection is an AdaBoost classifier based on Haar features; (b) the second layer of detection is a support vector machine (SVM) classifier based on histogram of oriented gradient (HOG) features; (c) the third layer of detection is the threshold judgment of the three features. We use this method to detect and count whiteback planthoppers (Sogatella furcifera) on rice plant images and achieve an 85.2% detection rate and a 9.6% false detection rate. The method is easy, rapid and accurate for the assessment of the population density of rice planthoppers in paddy fields.

关键词: insect counting , rice planthoppers , handheld device , AdaBoost classifier , SVM classifier , image features

Abstract: A quantitative survey of rice planthoppers in paddy fields is important to assess the population density and make forecasting decisions. Manual rice planthopper survey methods in paddy fields are time-consuming, fatiguing and tedious. This paper describes a handheld device for easily capturing planthopper images on rice stems and an automatic method for counting rice planthoppers based on image processing. The handheld device consists of a digital camera with WiFi, a smartphone and an extrendable pole. The surveyor can use the smartphone to control the camera, which is fixed on the front of the pole by WiFi, and to photograph planthoppers on rice stems. For the counting of planthoppers on rice stems, we adopt three layers of detection that involve the following: (a) the first layer of detection is an AdaBoost classifier based on Haar features; (b) the second layer of detection is a support vector machine (SVM) classifier based on histogram of oriented gradient (HOG) features; (c) the third layer of detection is the threshold judgment of the three features. We use this method to detect and count whiteback planthoppers (Sogatella furcifera) on rice plant images and achieve an 85.2% detection rate and a 9.6% false detection rate. The method is easy, rapid and accurate for the assessment of the population density of rice planthoppers in paddy fields.

Key words: insect counting , rice planthoppers , handheld device , AdaBoost classifier , SVM classifier , image features