NEURAL-NETWORK CLASSIFICATION OF WHEAT USING SINGLE KERNEL NEAR-INFRARED TRANSMITTANCE SPECTRA

To investigate an accurate, rapid, and nondestructive method for wheat classification in inspection terminals, backpropagation neural networ k models were developed, based on single wheat kernel near-infrared tr ansmittance 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 mod els with 200 input nodes, the average classification accuracy was 97% to 100%. For the six-class model with 200 input nodes, the average acc uracy was 94.7%. The classification between hard red winter (HRW) and hard red spring (HRS) was least accurate among the six classes. For ra pid classification, a narrower wavelength range, 899 to 1049 nm, with an interval of 2 nm, was proposed and shown to have little loss in acc uracy, The most time-consuming two-class (HRW-HRS) model could be cali brated and validated in less than 7 min. Prediction for new data was n early instantaneous. A backpropagation neural network model with a lea rning coefficient of 0.6 to 0.65 and momentum of 0.4 to 0.45, without a hidden layer, was effective for wheat classification.