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

文献类型： 外文期刊

第一作者： Yao Qing

作者： Yao Qing;Chen Guo-te;Wang Zheng;Zhang Chao;Yang Bao-jun;Tang Jian

作者机构：

关键词： white-backed planthopper;developmental stage;automated detection and identification;image processing;histogram of oriented gradient features;gabor features;local binary pattern 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： 2017 年 16 卷 7 期

页码：

收录情况： SCI

摘要： 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 planthoppei.s (WBPHs, Sogatella furcifera (Horvath)) 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.

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