Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderiacepacia)-infected onions

► An SWIR hyperspectral imaging system was explored to detect sour skin in onions. ► Healthy & infected onions showed distinct spectral characteristics in 950–1650nm. ► Log-ratio images of onions were generated using images at 1070 & 1400nm. ► SVM using image features (max, contrast, & homogeneity) achieved 87.14% accuracy. ► The study shows the potential of using spectral imaging to detect onion sour skin. Sour skin (Burkholderiacepacia) is a major postharvest disease for onions and causes substantial production and economic losses in onion postharvest. In this study, a shortwave infrared hyperspectral imaging system was explored to detect sour skin. The hyperspectral reflectance images (950–1650nm) of onions were obtained for the healthy and sour skin-infected onions. Principal component analysis conducted on the spectra of the healthy and sour skin-infected onions suggested that the neck area of the onion at two wavelengths (1070 and 1400nm) was most indicative of the sour skin. Log-ratio images utilizing the two optimal wavelengths were used for two different image analysis approaches. The first method applied a global threshold (0.45) to segregate the sour skin-infected areas from log-ratio images. Using the pixel number of the segregated areas, Fisher’s discriminant analysis recognized 80% healthy and sour skin-infected onions. The second classification approach used three parameters (max, contrast, and homogeneity) of the log-ratio images as the input features of support vector machine (Gaussian kernel, γ=1.5), which discriminated 87.14% healthy and sour skin-infected onions. The result of this study can be used to further develop a multispectral imaging system to detect sour skin-infected onions on packing lines.