Use of genetic artificial neural networks and spectral imaging for defect detection on cherries

A machine vision system was created to identify different types of tissue characteristics on cherries. It consists of an enhanced NIR range vidicon black and white camera (sensing range 400¯2000 nm), a monochrometer controlled light source, and a computer. Multiple spectral images of cherry samples were collected over the 680¯1280 nm range at increments of 40 nm. Using the spectral signatures of different tissues on cherry images, artificial neural networks were applied to pixel-wise classification. An enhanced genetic algorithm was applied to design the topology and evolve the weights for multi-layer feed forward artificial neural networks. An average of 73% classification accuracy was achieved for correct identification as well as quantification of all types of cherry defects. No false positives or false negatives occurred, errors resulted only from misclassification of defect type or quantification of defect.