French fries are one of the frozen foods with rising demands in domestic and international markets. Color is one of the critical attributes for quality evaluation of french fries. This study discusses the development of a color computer vision system and the integration of neural network technology for objective color evaluation and classification of french fries. The classification accuracy of a prototype back-propagation network developed for this purpose was found to be 96%.
A color classification program was developed for classifying the corn germplasm into seven different color groups based on kernel colors. This heuristic based rule supervised color classification program has an overall accuracy of 99%.