Classification of Processing Asparagus Sections Using Color Images
Impartial classification of Asparagus sections (Asparagus officinalis L.), for the purpose of obtaining desired tip to stem pieces ratio in final product, is extremely important to the processing industry. Thus, there is a need to develop a technique that is able to objectively discern between tip and stem pieces, after asparagus has been processed (cut). In this article, a computer vision methodology is proposed to sort asparagus into three classes: tips, mid-stem pieces and bottom-stem pieces. Nine hundred and fifty-five color images from 50 mm length asparagus pieces (cuts) for the three different classes were acquired, using a flat panel scanner. After preprocessing, a total of 1931 color, textural, and geometric features were extracted from each color image. The most relevant features were selected using a sequential forward selection algorithm. Forty-three features were found to be effective in designing a neural-network classifier with a 4-fold cross-validated overall performance accuracy of 90.2% (±2.2%). Results showed that this method is an accurate, reliable, and objective tool to discern between asparagus tips, mid-stem and bottom pieces, and might be applicable to in-line sorting systems.