Buranasiri, Prathan

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Demonstration of a single-wavelength spectral-imaging-based Thai jasmine rice identification
(2011-07-20)
Suwansukho, Kajpanya
;
Sumriddetchkajorn, Sarun
;
Buranasiri, Prathan
A single-wavelength spectral-imaging-based Thai jasmine rice breed identification is demonstrated. Our nondestructive identification approach relies on a combination of fluorescent imaging and simple image processing techniques. Especially, we apply simple image thresholding, blob filtering, and image subtracting processes to either a 545 or a 575nm image in order to identify our desired Thai jasmine rice breed from others. Other key advantages include no waste product and fast identification time. In our demonstration, UVC light is used as our exciting light, a liquid crystal tunable optical filter is used as our wavelength seclector, and a digital camera with 640 active pixels × 480 active pixels is used to capture the desired spectral image. Eight Thai rice breeds having similar size and shape are tested. Our experimental proof of concept shows that by suitably applying image thresholding, blob filtering, and image subtracting processes to the selected fluorescent image, the Thai jasmine rice breed can be identified with measured false acceptance rates of <22:9% and <25:7% for spectral images at 545 and 575nm wavelengths, respectively. A measured fast identification time is 25ms, showing high potential for real-time applications. © 2011 Optical Society of America.
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Improvement of single wavelength-based Thai jasmine rice identification with elliptic Fourier descriptor and neural network analysis
(2012-12-01)
Suwansukho, Kajpanya
;
Sumriddetchkajorn, Sarun
;
Buranasiri, Prathan
Instead of considering only the amount of fluorescent signal spatially distributed on the image of milled rice grains this paper shows how our single-wavelength spectral-imaging-based Thai jasmine (KDML105) rice identification system can be improved by analyzing the shape and size of the image of each milled rice variety especially during the image threshold operation. The image of each milled rice variety is expressed as chain codes and elliptic Fourier coefficients. After that, a feed-forward back-propagation neural network model is applied, resulting in an improved average FAR of 11.0% and FRR of 19.0% in identifying KDML105 milled rice from the unwanted four milled rice varieties. © Copyright SPIE.
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Fast fluorescent imaging-based Thai jasmine rice identification with polynomial fitting function and neural network analysis
(2014-04-01)
Suwansukho, Kajpanya
;
Sumriddetchkajorn, Sarun
;
Buranasiri, Prathan
With our single-wavelength spectral-imaging-based Thai jasmine rice identification system, we emphasize here that a combination of an appropriate polynomial fitting function on the determined chain code and a well-trained neural network configuration is highly sufficient in achieving a low false acceptance rate (FAR) and a low false rejection rate (FRR). Experimental demonstration shows promising results in identifying our desired Thai jasmine rice from six unwanted rice varieties with FAR and FRR values of 6.2% and 7.1%, respectively. Additional key performances include a much faster identification time of 30.5 s, chemical-free analysis, robustness, and adaptive learning.
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Identification of Thai hom mali rice using a refractometer
(2009-09-08)
Sumriddetchkajorn, Sarun
;
Suwansukho, Kajpanya
;
Buranasiri, Prathan
Because Thai Hom Mali, also known as Thai Dawk Mali (KDML105), rice is very popular and its price is high compared to other Thai rice varieties, there is an increase in mixing KDML105 milled and unmilled rice grains with other rice varieties, leading to unqualified KDML105 milled rice products for export and unqualified KDML105 unmilled rice seeds for next plants. Instead of using traditional time- and energy- consuming procedures such as alkaline spreading value and pasting property tests, this paper proposes a fast refractometry-based method to analyze ground milled rice grains dissolved in an alkaline solution. Our idea comes from the fact that due to differences in the amount of amylose content in each rice variety, the refractive index of the milled rice powder dissolved in an alkaline solution can be used to distinguish the desired KDML105 rice from others. In our approach, only 0.1 grams of milled rice powder is ground, it is then dissolved in a 10% potassium hydroxide, and its refractive index is investigated. Our experiment using a temperature-controlled optical refractometer and four Thai rice varieties (KDML105, Pathumthani1, Chainat1, and a Thai sticky rice) shows that the milled KDML105 rice can be distinguished from the remaining three rice varieties with a total false error rate of 6.7% and the required measurement time of < 20 seconds. Key advantages include simplicity, moderate accuracy, and less waste produced. © 2009 SPIE.
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Single-wavelength based thai jasmine rice identification with polynomial fitting function and neural network analysis
(2013-09-18)
Suwansukho, Kajpanya
;
Sumriddetchkajorn, Sarun
;
Buranasiri, Prathan
We previously showed that a combination of image thresholding, chain coding, elliptic Fourier descriptors, and artificial neural network analysis provided a low false acceptance rate (FAR) and a false rejection rate (FRR) of 11.0% and 19.0%, respectively, in identify Thai jasmine rice from three unwanted rice varieties. In this work, we highlight that only a polynomial function fitting on the determined chain code and the neural network analysis are highly sufficient in obtaining a very low FAR of < 3.0% and a very low 0.3% FRR for the separation of Thai jasmine rice from Chainat 1 (CNT1), Prathumtani 1 (PTT1), and Hom-Pitsanulok (HPSL) rice varieties. With this proposed approach, the analytical time is tremendously suppressed from 4,250 seconds down to 2 seconds, implying extremely high potential in practical deployment. © 2013 SPIE.
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Buranasiri, Prathan

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