Classification of Fungal-Damaged Soybean Seeds Using Near-Infrared Spectroscopy

Abstract

Abstract Fungal damage has a devastating impact on soybean quality and end-use. The current visual method for identifying damaged soybean seeds is based on discoloration and is subjective. The objective of this research was to classify healthy and fungal-damaged soybean seeds and discriminate among various types of fungal damage using near-infrared (NIR) spectroscopy. A diode-array NIR spectrometer, which measured reflectance [log(1/R)] from 400 to 1700 nm, was used to obtain spectra from single soybean seeds. Partial least square (PLS) and neural network models were developed to differentiate healthy and fungal-damaged seeds. The highest classification accuracy was more than 99% when the wavelength region of 490–1690 nm was used under a two-class PLS model. Neural network models yielded higher classification accuracy than the PLS models for five-class classification. The average of correct classifications was 93.5% for the calibration sample set and 94.6% for the validation sample set. Classification accuracies of the validation sample set were 100, 99, 84, 94, and 96% corresponding to healthy seeds, Phomopsis, Cercospora kikuchii, soybean mosaic virus (SMV), and downy mildew damaged seeds, respectively.