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Detection of small-sized insect pest in greenhouses based on multifractal analysis
Li Y(李岩); Xia, Chunlei; Lee, Jangmyung
Department水下机器人研究室
Source PublicationOptik
ISSN0030-4026
2015
Volume126Issue:19Pages:2138-2143
Indexed BySCI ; EI
EI Accession number20152700989024
WOS IDWOS:000364604200080
Contribution Rank1
Funding OrganizationNational Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (grant no. NRF-2013R1A1A2021174).
KeywordMultifractal Analysis Robot Vision Image Processing Pest Detection Small-size Pest
AbstractA new application of multifractal analysis for the detection of small-sized pests (e.g., whitefly) from leaf surface images in situ is proposed in this paper. Multifractal analysis was adopted for segmentation of whitefly images based on the local singularity and global image characters with the regional minima selection strategy. According to the multifractal dimension, the candidate blobs of whiteflies were initially defined from the leaf image. The regional minima were utilized for feature extraction of candidate whitefly image areas and the performance was compared to that of the fixed threshold. Subsequently, most false alarms from leaf veins were decreased by consideration of the size and shape of the whiteflies. Experiments were conducted with field images in a greenhouse. Detection results were compared with other adaptive segmentation algorithms. Values of F measuring precision and recall scores were higher for the proposed multifractal analysis (88.6%) than for conventional methods such as Watershed (60.2%) and Efficient Graph-based Image Segmentation (EGBIS; 42.8%). The true-positive rate of multifractal analysis was 86.9% and the false-positive rate was at the minimum level of 8.2%. Overall, the detection of small-sized pests is most feasible with the proposed multifractal analysis under greenhouse conditions.
Language英语
WOS HeadingsScience & Technology ; Physical Sciences
WOS SubjectOptics
WOS KeywordIMAGE SEGMENTATION ; MACHINE VISION ; MANAGEMENT ; EXTRACTION ; ALGORITHM ; CROPS ; GRAIN
WOS Research AreaOptics
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/16465
Collection水下机器人研究室
Corresponding AuthorLee, Jangmyung
Affiliation1.Department of Electrical Engineering, Pusan National University, Busan, Korea, Republic of
2.SPENALO Robot Technology Research Center, Pusan National University, Busan, Korea, Republic of
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Li Y,Xia, Chunlei,Lee, Jangmyung. Detection of small-sized insect pest in greenhouses based on multifractal analysis[J]. Optik,2015,126(19):2138-2143.
APA Li Y,Xia, Chunlei,&Lee, Jangmyung.(2015).Detection of small-sized insect pest in greenhouses based on multifractal analysis.Optik,126(19),2138-2143.
MLA Li Y,et al."Detection of small-sized insect pest in greenhouses based on multifractal analysis".Optik 126.19(2015):2138-2143.
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