Fast detection of the early decay in oranges using visible-LED structuredillumination imaging combined with spiral phase transform and feature-based classification model

The early decay of citrus can cause economic and serious food safety issues.The early decayed area has no obvious visual characteristics,making effective detection of this damage very difficult for the citrus industry.This study constructed a new detection system based on visible-light emitting diode(LED)structured-illumination imaging and proposed an effective methodology combined with a spiral phase transform(SPT)algorithm for the early detection of decayed oranges.Each sample obtained three phase-shifting pattern images with phase shifts of−2π/3,0,and 2π/3 at a spatial frequency of 0.25 cycles/mm.Three strategies(i.e.,the conventional three-phase-shifting method,2-phase SPT,and 1-phase SPT)were used to demodulate the original patterned images to recover the direct component(DC)and amplitude component(AC)images.The partial least squares discriminant analysis(PLS-DA)and least squares support vector machine(LS-SVM)classification models were established based on the texture features of DC,AC,and RT(i.e.the ratio of AC to DC)images.Then,the random frog(RF)algorithm was used to simplify the optimal full-featured model.Finally,the LS-SVM model constructed using 7 texture features from the RT image obtained an average classification accuracy of 95.1%for all tested samples.This study indicates that the proposed structured-illumination imaging technique combined with 2-phase SPT and feature-based classification model can achieve the fast identification of early decayed oranges.

Enhanced detection algorithm for apple bruises using structured light imaging

Bruising reduces the edibility and marketability of fresh apples,inevitably causing economic losses for the apple industry.However,bruises lack obvious visual symptoms,which makes it challenging to detect them using imaging techniques with uniform or diffuse illumination.This study employed the structured light imaging(SLI)technique to detect apple bruises.First,the grayscale reflection images were captured under phase-shifted sinusoidal illumination at three different wavelengths(600,650,and 700 nm)and six different spatial frequencies(0.05,0.10,0.15,0.20,0.25,and 0.30 cycles mm−1).Next,the grayscale reflectance images were demodulated to produce direct component(DC)images representing uniform diffuse illumination and amplitude component(AC)images revealing bruises.Then,by quantifying the contrast between bruised regions and sound regions in all AC images,it was found that bruises exhibited the optimal contrast when subjected to sinusoidal illumination at a wavelength of 700 nm and a spatial frequency of 0.25 mm−1.In the AC image with optimal contrast,the developed h-domes segmentation algorithm to accurately segment the location and range of the bruised regions.Moreover,the algorithm successfully accomplished the task of segmenting central bruised regions while addressing the challenge of segmenting edge bruised regions complicated by vignetting.The average Intersection over Union(IoU)values for the three types of bruises were 0.9422,0.9231,and 0.9183,respectively.This result demonstrated that the combination of SLI and the h-domes segmentation algorithm was a viable approach for the effective detection of fresh apple bruises.