Fast fluorescent imaging-based Thai jasmine rice identification with polynomial fitting function and neural network analysis.
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.
Kajpanya
K
Suwansukho
Sarun
S
Sumriddetchkajorn
Prathan
P
Buranasiri
D000465:Algorithms; D005504:Food Analysis / Q000379:methods*; D005524:Food Technology / Q000379:methods; D008856:Microscopy, Fluorescence / Q000379:methods; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D012275:Oryza / Q000737:chemistry* / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013050:Spectrometry, Fluorescence / Q000379:methods*