1 October 1995 Neural network classification of wheat using single kernel near-infrared transmittance spectra
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Optical Engineering, 34(10), (1995). doi:10.1117/12.210745
To investigate an accurate, rapid, and nondestructive method for wheat classification in inspection terminals, backpropagation neural network models were developed, based on single wheat kernel near-infrared transmittance spectra. Six classes of wheat were studied. Neural network models were optimized for two-class and six-class classification. The wavelength range of the spectra was 850 to 1049 nm. For two-class models with 200 input nodes, the average classification accuracy was 97% to 100%. For the six-class model with 200 input nodes, the average accuracy was 94.7%. The classification between hard red winter (HRW) and hard red spring (HRS) was least accurate among the six classes. For rapid classification, a narrower wavelength range, 899 to 1049 nm, with an interval of 2 nm, was proposed and shown to have little loss in accuracy. The most time-consuming two-class (HRW-HRS) model could be calibrated and validated in less than 7 mm. Prediction for new data was nearly instantaneous. A backpropagation neural network model with a learning coefficient of 0.6 to 0.65 and momentum of 0.4 to 0.45, without a hidden layer, was effective for wheat classification.
Huaipu Song, Stephen R. Delwiche, Yud-Ren Chen, "Neural network classification of wheat using single kernel near-infrared transmittance spectra," Optical Engineering 34(10), (1 October 1995). http://dx.doi.org/10.1117/12.210745


Neural networks


Near infrared

Data modeling



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