首页|Detection of insect damaged rice grains using visible and near infrared hyperspectral imaging technique
Detection of insect damaged rice grains using visible and near infrared hyperspectral imaging technique
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2021 Elsevier B.V.The visible near infrared hyperspectral imaging systems (HIS) with a xenon light source, Pika XC2 camera having a spectral range of 400–1100 ?nm, and a SpectrononPro software was used for the hypercube data visualization of the fresh and the damaged rice grains. The linear assembly of stage control was set with a scanning speed of 0.79 ?cm/s, homing speed of 0.77 ?cm/s, and a stepping mode of 0.60 ?cm/s. The captured images in the form of RGB data cubes were modified in MATLAB 2017a to gray image, and then further to a binary image. Dimensional reduction using PCA was at first applied to the range of wavelengths of 396.16 ?nm–1003.71 ?nm to obtain the first and second principal component versus wavelength graphs. The images were then cropped and masked in MATLAB to get first versus second principal component plots for both damaged and healthy rice grains. The first and second principal components have a mean value of 699.9 ?nm, and a mode value of 396.2 ?nm in the case of fresh rice grains, and a mean value of 700.1 ?nm, and a mode value of 396.2 ?nm for the damaged rice grains. The cropping of the images was then at significant wavelengths of 904.07, 914.90, 646.32, and 725.38 ?nm for the fresh rice grains, while 910.57, 916.49, 691.80, and 852.63 ?nm for the damaged rice grains respectively. The standard error reported for the fresh rice on the X-axis (XF) and Y-axis (YF) was 1.34 (XF), and 0.17 (YF), while for the damaged rice was 1.15 (XI), and 0.15 (YI) respectively. Therefore, it can be affirmed that the prediction or distinction of rice on the basis of fresh and damaged ones can be done with ease. Further, this approach can be applied to unknown samples to detect insect infestation in rice.
NSTL
Elsevier
