Postharvest Noninvasive Classification of Tough-fibrous Asparagus Using Computed Tomography Images
This research was devised to evaluate Computed Tomography (CT) for asparagus fibrousness detection and more specifically develop and test an automatic image analysis method (algorithm) to classify CT images obtained from 859 asparagus (Asparagus officinalis L.) segment (samples), collected during two harvesting seasons (2014 and 2015). Classification accuracy was calculated by comparing the classes obtained using a combination of imaging, image processing, feature extraction, and classification schemes per asparagus segment against an industry-simulated invasive quality assessment.
Grayscale intensity and textural features, 3762 total, were extracted from minimum and maximum resultant images from three CT planer views. A 4-fold cross-validation linear discriminant classifier with a performance accuracy of 91.2% was developed using 75 relevant features, which were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. This objective method is accurate in determining the presence of tough-fibrous tissue in asparagus, which demonstrates a potential for such technology to objectively forecast asparagus quality and thus supports the asparagus industry through optimizing consumer acceptability and product utilization