National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (grant no. NRF-2013R1A1A2021174).
A 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.