文献类型： 外文期刊
作者： Yao Qing 1 ; Lv Jun 1 ; Liu Qing-jie 1 ; Diao Guang-qiang 1 ; Yang Bao-jun 2 ; Chen Hong-ming 3 ; Tang Jian 2 ;
作者机构： 1.Zhejiang Sci Tech Univ, Coll Informat & Elect, Hangzhou 310018, Zhejiang, Peoples R China
2.China Natl Rice Res Inst, State Key Lab Rice Biol, Hangzhou 310006, Zhejiang, Peoples R China
3.Xiangshan Agr & Forestry Bur, Ningbo 315700, Zhejiang, Peoples R China
关键词： automatic identification;imaging system;rice light-trap pests;SVM;cross-validate
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： 2012 年 11 卷 6 期
页码：
收录情况： SCI
摘要： Identification and counting of rice light-trap pests are important to monitor rice pest population dynamics and make pest forecast. Identification and counting of rice light-trap pests manually is time-consuming, and leads to fatigue and an increase in the error rate. A rice light-trap insect imaging system is developed to automate rice pest identification. This system can capture the top and bottom images of each insect by two cameras to obtain more image features. A method is proposed for removing the background by color difference of two images with pests and non-pests. 156 features including color, shape and texture features of each pest are extracted into an support vector machine (SVM) classifier with radial basis kernel function. The seven-fold cross-validation is used to improve the accurate rate of pest identification. Four species of Lepidoptera rice pests are tested and achieved 97.5% average accurate rate.
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