This research was designed to develop and test an automatic image analysis method (algorithm) to classify CT images obtained from 1233 carrot (Daucus carota L.) sections (samples), collected during the 2013 and 2014 harvesting seasons. Classification accuracy was evaluated by comparing the classes obtained using eighteen CT images per carrot section to their undesirable fibrous tissue class, based on the industry-simulated invasive quality assessment (% of fiber). Class-0 represents fibrous-free samples, and class-1 denotes samples containing fibrous tissue. After CT image preprocessing, cropping, and segmentation, 3762 grayscale intensity and textural features were extracted from the eighteen CT images per sample. A 4-fold cross-validation linear discriminant classifier with a performance accuracy of 87.9% was developed using 95 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 undesirable fibrous tissue in pre-processed carrots.
Donis-González, I. R., Guyer, D. E., & Pease, A. (2016). Postharvest noninvasive assessment of undesirable fibrous tissue in fresh processing carrots using computer tomography images. Journal of Food Engineering, 190, 154–166. https://doi.org/10.1016/j.jfoodeng.2016.06.024