Abstract
Blueberries are prone to internal bruising damage during harvesting and postharvest handling. Accurate assessment of bruising damage improves profitability by allowing allocation of berries to appropriate product streams. The goal of this study was to develop a pulsed thermographic imaging system and explore its feasibility in non-destructively detecting bruised blueberries. In this paper, the design and construction of a pulsed thermographic imaging system was described. A total of 200 blueberry fruit samples from two southern highbush cultivars (Farthing and Meadowlark) were collected and bruising treatments were applied to half of the samples. Relevant features were extracted and were demonstrated to be significantly different between healthy and bruised fruit. Classification was performed using linear discriminant analysis, support vector machine, random forest, k-nearest-neighbors, and logistic regression classifiers. Accuracies of up to 88% and 79% were obtained for Farthing and Meadowlark berries, respectively. These results demonstrate the feasibility of pulsed thermography to discriminate between bruised and healthy blueberries.
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