Whole-surface round object imaging method using line-scan hyperspectral imaging system

Author(s):  
I. Baek ◽  
S. A. Gadsden ◽  
B. K. Cho ◽  
H. Lee ◽  
M. S. Kim
2010 ◽  
Author(s):  
Seung Chul Yoon ◽  
Bosoon Park ◽  
Kurt C. Lawrence ◽  
William R. Windham ◽  
Gerald W. Heitschmidt

2019 ◽  
Vol 16 (2) ◽  
pp. 143
Author(s):  
JR Lessy Eka Putri ◽  
Minarni Minarni ◽  
Feri Candra ◽  
Herman Herman

The hyperspectral imaging method has been widely and intensively used in agriculture to find out various problems that occur in plants. Image processing is very important step in an imaging method. This research aims to create Matlab based program to be used to analyze the leaf image of oil palm plants that has experienced water deficiency. Reflectance intensity values were extracted from the process. The hyperspectral imaging system consisted of a 650 nm diode laser, a spectrograph, monochrome CMOS camera, and Matlab image processing program. The samplesused were 8 month old Tenera variety of oil palm seedlings which were treated to simulate water deficiency in the form of variations in the volume of water, namely 0 mL (without watering), 1000 mL, 2000 mL, and 3000 mL (normal), 3 duplicates for each volume. The samples were given water volume of 1000 mL and 2000 mL for every 7 days in 21 days, while the sampleswith 3000 mL of water were watered every day. Image recording was done on the 21st day for detached leaves at the the bottom part.The results showed that the Matlab program was able to separate each row from 15 images, each of which had a pixel size of 1280 × 1024 and merge each of the same lines into 1024 images with a pixel size of 1280 × 15. The reflectance intensity values were then obtained. The results showed that higher levels of water deficiency in plants produce increasing reflectance intensity values.


2011 ◽  
Vol 79 (2) ◽  
pp. 159-168 ◽  
Author(s):  
Seung Chul Yoon ◽  
Bosoon Park ◽  
Kurt C. Lawrence ◽  
William R. Windham ◽  
Gerald W. Heitschmidt

2017 ◽  
Vol 71 (11) ◽  
pp. 2469-2476 ◽  
Author(s):  
Jianwei Qin ◽  
Moon S. Kim ◽  
Kuanglin Chao ◽  
Maria Gonzalez ◽  
Byoung-Kwan Cho

A high-throughput Raman chemical imaging method was developed for direct inspection of benzoyl peroxide (BPO) mixed in wheat flour. A 5 W, 785 nm line laser (240 mm long and 1 mm wide) was used as a Raman excitation source in a push-broom Raman imaging system. Hyperspectral Raman images were collected in a wavenumber range of 103–2881 cm−1 from dry wheat flour mixed with BPO at eight concentrations (w/w) from 50 to 6400 ppm. A sample holder with a sampling volume of 150 × 100 × 2 mm3 was used to present a thin layer (2 mm thick) of the powdered sample for line-scan image acquisition with a spatial resolution of 0.2 mm. A baseline correction method based on adaptive iteratively reweighted penalized least squares was used to remove the fluctuating fluorescence signals from the wheat flour. To isolate BPO particles from the flour background, a simple thresholding method was applied to the single-band fluorescence-free images at a unique Raman peak wavenumber (i.e., 1001 cm−1) preselected for the BPO detection. Chemical images were created to detect and map the BPO particles. Limit of detection for the BPO was estimated in the order of 50 ppm, which is on the same level with regulatory standards. Pixel concentrations were calculated from the percentages of the BPO pixels in the chemical images. High correlation was found between the pixel concentrations and the mass concentrations of the BPO, indicating that the Raman chemical imaging method can be used for quantitative detection of the BPO mixed in the wheat flour.


Photonics ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 91
Author(s):  
Shuyue Zhan ◽  
Weiwen Zhou ◽  
Xu Ma ◽  
Hui Huang

Hyperspectral imaging remote sensing is mutually restricted in terms of spatial and spectral resolutions, signal-to-noise ratio and exposure time. To deal with this trade-off properly, it is beneficial for imaging systems to have high light flux. In this paper, we put forward a novel hyperspectral imaging method with high light flux bioinspired by chromatic blur vision in color blind animals. We designed a camera lens with high degree of longitudinal chromatic aberration, a monochrome image sensor captured the chromatic blur images at different focal lengths. Finally, by using the known point spread functions of the chromatic blur imaging system, we process these chromatically blurred images by deconvolution based on singular value decomposition inverse filtering, and the spectral images of a target were restored. We constructed three different targets for validating image restoration based on a typical octopus eyeball imaging system. The results show that the proposed imaging method can effectively extract spectral images from the chromatically blurred images. This study can facilitate development of a novel bionic hyperspectral imaging, which may benefit from the high light flux of a large aperture and provide higher detection sensitivity.


2020 ◽  
Vol 4 (3) ◽  
pp. 761
Author(s):  
Dina Veranita ◽  
Minarni Minarni ◽  
Feri Candra ◽  
Saktioto Saktioto ◽  
Mohammad Fisal Rabin

Hyperspectral imaging is a non destructive method that has been used to evaluate internal characteristics of fruits and vegetables. Plant genetics, soil characteristics, and plant management are some of key factors to define the quality of oil palm fresh fruit bunches (FFB) produced. This research was aimed to discriminate the Tenera oil palm FFBs produced by oil palm trees grown from mineral soil and peat soil using a hyperspectral imaging system which utilized a Specim V10 spektrograf. The discrimination was based on their ripeness level, mesocarp firmness, and classification using K-mean clustering. The samples consisted of 61 mineral soil FFBs and 60 peat soil FFBs with three ripeness levels as unripe, ripe, and overripe. Hyperspectral images were recorded and processed using Matlab programs. The spectral reflectance intensities showed the discrimination between both origin soils at wavelength ranges of 700 nm  900 nm. The results also showed higher reflectance intensities of peat soil FFBs than mineral soil FFBs. Correspondingly, Fruit firmness of peat soil FFBs are higher than mineral soil FFBs. Classification using K- mean clustering between reflectance intensities and fruit firmness showed significant clusters for three ripeness levels. These results will be useful for an oil palm FFB sorting machine based on spectral imaging method


LWT ◽  
2021 ◽  
Vol 138 ◽  
pp. 110678
Author(s):  
Irina Torres ◽  
Dolores Pérez-Marín ◽  
Miguel Vega-Castellote ◽  
María-Teresa Sánchez

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