Postharvest noninvasive assessment of fresh chestnut (Castanea spp.) internal decay using computer tomography images

Abstract

X-ray computed tomography (CT) is an effective noninvasive tool to visualize fresh agricultural commodities’ internal components and quality attributes, including those of chestnuts (Castanea spp). There is no procedure to automatically, effectively and efficiently classify fresh commodities from a continuous inline flow through a CT system. If the information obtained by CT scanning of fresh agricultural commodities is to be used in an industrial application (e.g. inline sorting), automated interpretation of CT images is essential. For this purpose, an image analysis method (algorithm) for the automatic classification of CT images obtained from 2848 fresh chestnuts (cv. ‘Colossal’ and ‘Chinese seedlings’), during the harvesting years from 2009 to 2012, was developed and tested. Classification accuracy was evaluated by comparing the classes obtained from six CT images per chestnut to their internal quality assessment. An experienced human rater performed internal quality assessment by visually and invasively rating fresh chestnut internal decay severity (quality) into 5-, 3- and 2-classes.After CT image preprocessing, cropping and segmentation, 1194 grayscale intensity and textural features were extracted from six resultant CT images per sample. Relevant features were selected using a sequential forward selection algorithm with the Fisher discriminant objective function. 86, 155 and 126 features were effective in designing a quadratic discriminant classifier with a 4-fold cross-validation with a performance accuracy of 85.9%, 91.2% and 96.1% for 5, 3 and 2 classes, respectively. This method is accurate and objective in determining fresh chestnut internal quality, and the methodology is applicable to automatic noninvasive inline CT sorting system development.