Classification of Damaged Soybean Seeds Using Near–infrared Spectroscopy

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 diode–array NIR spectrometer, which measured reflectance spectra (log[1/R]) from 400 to 1,700 nm, was used to collect single–seed 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 cross–validation 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 two–class 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 six–class 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.