Wheat Class Identification Using Thermal Imaging

Abstract

Wheat classes and varieties are determined by trained professionals in the laboratory. Several approaches have been made using machine vision technology for nondestructive and online identification of wheat classes, but the performance has been poor and inconsistent. An infrared thermal imaging system was developed to identify eight western Canadian wheat classes. Samples of 20 g each of wheat at 14% moisture content (wet basis) spread in a 100 × 100 mm monolayer were heated by a plate heater (maintained at 90 °C) placed at a distance of 10 mm from the grain layer. The surface temperatures of the top surface of the grain bulk were imaged before heating, after heating for 180 s, and after cooling for 30 s using an infrared thermal camera (n = 100). Temperature rise (after heating) and drop (after cooling) were significantly different for wheat classes (α = 0.05). The temperature rise ranged from 14.94 (Canada Western Red Spring) to 17.80 °C (Canada Prairie Spring Red), and the drop ranged from 3.67 (Canada Western Extra Strong) to 4.42 °C (Canada Prairie Spring Red) after heating for 180 s and cooling for 30 s, respectively. The rate of heating and cooling was negatively correlated with protein content of wheat (r = −0.63 for heating, r = −0.65 for cooling) and true density (r = −0.67 for heating, r = −0.71 for cooling), and positively correlated with grain hardness (r = +0.41 for heating, r = +0.53 for cooling). Overall classification accuracies of an eight-class model, red-class model (four classes), white-class model (four classes), and pairwise (two-class model) comparisons using a quadratic discriminant method were 76%, 87%, 79%, and 95%, and 64%, 87%, 77%, and 91% using bootstrap and leave-one-out validation methods, respectively. There were several misclassifications in the four and eight-class models. Thermal imaging approach may have potential to develop classification methods for two classes, which are similar and difficult to distinguish by visual inspection; however, the effect of growing season, defects, and kernel size must be considered while developing such methods. The temperature rise after heating and drop after cooling were tested for Canada Western Red Spring wheat at three moisture levels (11%, 14%, and 17% wet basis; n = 20). There were no significant differences (α = 0.05) in the mean temperature rise and temperature drop between 11%, 14%, and 17% moisture samples.