In situ detection of small-size insect pests sampled on traps using multifractal analysis

We introduce a multifractal analysis for detecting the small-size pest (e.g., whitefly) images from a sticky trap in situ. An automatic attrac- tion system is utilized for collecting pests from greenhouse plants. We applied multifractal analysis to segment action of whitefly images based on the local singularity and global image characteristics. According to the theory of multifractal dimension, the candidate blobs of whiteflies are initi- ally defined from the sticky-trap image. Two schemes, fixed thresholding and regional minima obtainment, were utilized for feature extraction of candidate whitefly image areas. The experiment was conducted with the field images in a greenhouse. Detection results were compared with other adaptive segmentation algorithms. Values of F measuring precision and recall score were higher for the proposed multifractal analysis (96.5%) compared with conventional methods such as Watershed (92.2%) and Otsu (73.1%). The true positive rate of multifractal analysis was 94.3% and the false positive rate minimal level at 1.3%. Detection performance was further tested via human observation. The degree of scattering between manual and automatic counting was remarkably higher with mul- tifractal analysis (R 2 ¼ 0.992) compared with Watershed (R 2 ¼ 0.895) and Otsu (R 2 ¼ 0.353), ensuring overall detection of the small-size pests is most feasible with multifractal analysis in field conditions. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). (DOI: 10.1117/ 1.OE.51.2.027001)