Automated Counting of Rice Planthoppers in Paddy Fields Based on Image Processing
文献类型： 外文期刊
作者： Yao Qing 1 ; Xian Ding-xiang 1 ; Liu Qing-jie 1 ; Yang Bao-jun 2 ; Diao Guang-qiang 1 ; Tang Jian 2 ;
作者机构： 1.Zhejiang Sci Tech Univ, Sch Informat Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China
2.China Natl Rice Res Inst, State Key Lab Rice Biol, Hangzhou 310006, Zhejiang, Peoples R China
关键词： insect counting;rice planthoppers;handheld device;AdaBoost classifier;SVM classifier;image features
期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）
ISSN： 2095-3119
年卷期： 2014 年 13 卷 8 期
页码：
收录情况： SCI
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.
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