Journal Articles (All Issues)

Abstract

This work proposes a novel image based method to determine the artificial and natural ripening methods used to ripen the mangoes. Monitoring the quality of ripening is a herculean task because the variation cannot be easily identified by manual vision judgement. Hence an automated method using image processing and Artificial Neural Networks (ANN) is proposed here. Features are extracted from the images of the mangoes after pre-processing is done. This feature set is used to train the Multi-Layered Perceptron (MLP) model. Initially, the conventional rule called the Gradient steepest Descent Rule (GDR) is used for training the MLP. Since, satisfactory performance is not achieved; a hybrid model of the MLP with Black Widow Optimization (BWO) is used to identify the naturally and artificially ripened mangoes effectively.

References

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