• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Author:

Li, Wenyong (Li, Wenyong.) | Yang, Zhankui (Yang, Zhankui.) | Lv, Jiawei (Lv, Jiawei.) | Zheng, Tengfei (Zheng, Tengfei.) | Li, Ming (Li, Ming.) | Sun, Chuanheng (Sun, Chuanheng.)

Indexed by:

Scopus SCIE

Abstract:

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.

Keyword:

pest detection machine learning image processing small objects detection sticky trap

Author Community:

  • [ 1 ] [Li, Wenyong]Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
  • [ 2 ] [Yang, Zhankui]Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
  • [ 3 ] [Lv, Jiawei]Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
  • [ 4 ] [Zheng, Tengfei]Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
  • [ 5 ] [Li, Ming]Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
  • [ 6 ] [Sun, Chuanheng]Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
  • [ 7 ] [Yang, Zhankui]Beijing Univ Technol, Coll Comp Sci & Technol, Beijing, Peoples R China
  • [ 8 ] [Lv, Jiawei]Zhongkai Univ Agr & Engn, Coll Informat Sci & Technol, Guangzhou, Peoples R China
  • [ 9 ] [Zheng, Tengfei]Shanghai Ocean Univ, Coll Informat, Shanghai, Peoples R China

Reprint Author's Address:

Show more details

Related Keywords:

Related Article:

Source :

FRONTIERS IN PLANT SCIENCE

ISSN: 1664-462X

Year: 2022

Volume: 13

5 . 6

JCR@2022

5 . 6 0 0

JCR@2022

ESI Discipline: PLANT & ANIMAL SCIENCE;

ESI HC Threshold:25

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count: 13

SCOPUS Cited Count: 14

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

Affiliated Colleges:

Online/Total:554/10596450
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.