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CLASSIFICATION OF DAMAGED SOYBEAN SEEDS USING NEAR–INFRARED SPECTROSCOPY

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASAE. 45(6): 1943–1948. (doi: 10.13031/2013.11410) @2002
Authors:   D. Wang, M. S. Ram, F. E. Dowell
Keywords:   Damage, Near–infrared spectroscopy, Neural networks, Soybean seeds

Damage is an important quality factor for grading, marketing, and end use of soybean. Seed damage can be caused by weather, fungi, insects, artificial drying, and by mechanical damage during harvest, transportation, storage, and handling. The current visual method for identifying damaged soybean seeds is based on discoloration and is subjective. The objective of this research was to classify sound and damaged soybean seeds and discriminate among various types of damage using NIR spectroscopy. A diodearray NIR spectrometer, which measured reflectance spectra (log[1/R]) from 400 to 1,700 nm, was used to collect singleseed spectra. Partial least square (PLS) models and neural network models were developed to classify sound and damaged seeds. For PLS models, the NIR wavelength region of 490 to 1,690 nm provided the highest classification accuracy for both crossvalidation of the calibration sample set and prediction of the validation sample set. Classification accuracy of sound and damaged soybean seeds was higher than 99% when using a twoclass model. The classification accuracies of sound seeds and those damaged by weather, frost, sprout, heat, and mold were 90%, 61%, 72%, 54%, 84%, and 86%, respectively, when using a sixclass model. Neural network models yielded higher classification accuracy than PLS models. The classification accuracies of the validation sample set were 100%, 98%, 97%, 64%, 97%, and 83% for sound seeds and those damaged by weather, frost, sprout, heat, and mold, respectively, for the neural network model. The optimum parameters of the neural network model were learning rate of 0.7 and momentum of 0.6.

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