ANN analysis in a vision approach for potato inspection

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R. Rios-Cabrera
I Lopez-Juarez
Hsieh Sheng-Jen

Abstract

An image processing methodology for the extraction of potato properties is explained. The objective is to determine their quality evaluating physical properties and using Artificial Neural Networks (ANN’s) to find misshapen potatoes. A comparative analysis for three connectionist models (Backpropagation, Perceptron and FuzzyARTMAP), evaluating speed and stability for classifying extracted properties is presented. The methodology for image processing and pattern feature extraction is presented together with some results. These results showed that FuzzyARTMAP outperformed the other models due to its stability and convergence speed with times as low as 1 ms per pattern which demonstrates its suitability for real-time inspection. Several algorithms to determine potato defects such as greening, scab, cracks are proposed which can be affectively used for grading different quality of potatoes.

 

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How to Cite
Rios-Cabrera, R., Lopez-Juarez, I., & Sheng-Jen, H. (2008). ANN analysis in a vision approach for potato inspection. Journal of Applied Research and Technology, 6(02). https://doi.org/10.22201/icat.16656423.2008.6.02.521