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Detection of Small-Sized Insects in Sticky Trapping Images Using Spectral Residual Model and Machine Learning

文献类型： 外文期刊

作者： Li, Wenyong 1 ; Yang, Zhankui 1 ; Lv, Jiawei 1 ; Zheng, Tengfei 1 ; Li, Ming 1 ; Sun, Chuanheng 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.Beijing Univ Technol, Coll Comp Sci & Technol, Beijing, Peoples R China

3.Zhongkai Univ Agr & Engn, Coll Informat Sci & Technol, Guangzhou, Peoples R China

4.Shanghai Ocean Univ, Coll Informat, Shanghai, Peoples R China

关键词： pest detection; sticky trap; small objects detection; image processing; machine learning

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (Bemisia tabaci) and thrips (Frankliniella occidentalis) are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one. To alleviate this situation, this study proposed an automated detection approach to meet the need for continuous monitoring of pests in greenhouse conditions. Candidate targets were firstly located using a spectral residual model and then different color features were extracted. Ultimately, Whitefly and thrips were identified using a support vector machine classifier with an accuracy of 93.9 and 89.9%, a true positive rate of 93.1 and 80.1%, and a false positive rate of 9.9 and 12.3%, respectively. Identification performance was further tested via comparison between manual and automatic counting with a coefficient of determination, R-2, of 0.9785 and 0.9582. The results show that the proposed method can provide a comparable performance with previous handcrafted feature-based methods, furthermore, it does not require the support of high-performance hardware compare with deep learning-based method. This study demonstrates the potential of developing a vision-based identification system to facilitate rapid gathering of information pertaining to numbers of small-sized pests in greenhouse agriculture and make a reliable estimation of overall population density.

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