Utilizing machine vision and artificial neural networks for dried grape sorting during production Original scientific paper
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Abstract
This study introduces a machine vision technique that utilizes an artificial neural network (ANN) to develop a predictive model for classifying dried grapes during the drying process. The primary objective of this model is to mitigate the burden placed on the operator and minimize the occurrence of over-dried items. The present study involves the development of a model that is constructed using the characteristics of grape color and shape. There exist two distinct categories of labels for grapes: fully desiccated grapes, commonly referred to as raisins, and grapes that have undergone partial drying. Image processing is utilized to collect and observe five significant characteristics of grapes during the drying process. The findings indicate a significant decrease in the levels of red, green, and blue colors (RGB) during the initial 15-hour drying period. The predictive model extracts properties such as RGB color, roundness, and shrinkage from the image while it undergoes the drying process. The artificial neural network (ANN) model achieved a level of accuracy performance of 78%. In this work, the dehydration apparatus will cease operation in an automated manner whenever the entirety of the grapes situated on the tray have been projected to undergo the transformation into raisins.
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Funding data
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King Mongkut's University of Technology North Bangkok
Grant numbers KMUTNB-65-BASIC-32
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