Detection of Rot Blueberry Disease by Hyperspectral Imaging with SIS and RFS

In order to detect the rot disease of postharvest blueberries quickly, effectively and accurately, the rot disease of postharvest blueberries was detected by hyperspectral imaging technology with different detection models. According to analyse the difference of the spectral relative reflectance between the normal blueberries and the disease blueberries, the spectral Information segmentation(SIS) was proposed to segment the disease regions of blueberries to solve the problem that the conventional threshold segmentation method is difficult to accurately segment blueberry disease regions due to the indistinct color characteristics of the normal blueberries regions and the disease blueberries regions. According to the difference of spectrum in 450 nm to 1 000 nm, the regional feature selection(RFS) was put forward that divided the spectral relative reflectance(450-1 000 nm) into the two regions. The first region was in visible spectrum ranges 450-780 nm and the second region in near infrared ranges 780-1 000 nm.Then CARS and SPA were used to extract the characteristic wavelengths from spectral data in two regions. Finally, relevance vector machines(RVM) model and radial basis function(RBF) model were used to detect the rot disease of blueberries. By comparing the detection effects of different models, the CARS-RBF model in the combined regions of first region and second region had best detection effect and the characteristic wavelengths were 655.8, 710.9, 752.2, 759.9, 761.2, 866.5, 969.7 nm. The detection accuracy of the normal blueberries and the disease blueberries in the training sets and the testing sets were 98.3%, 98.6% and 97.5%, 98.75%, respectively. According to the result of the detection,we can draw a conclusion that the spectral information segmentation(SIS) and regional feature selection(RFS) were used to detect blueberry diseases effectively, which provide a new reference method for on-line detection and sorting of blueberries. © 2019, Science Press. All right reserved.