A Non-destructive Methodology for Determination of Cantaloupe Sugar Content using Machine Vision and Deep Learning.

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Abstract

To determine the maturity of cantaloupe, measuring the soluble solid content (SSC) as the indicator of sugar content based on the refractometric index is commonly practiced. This method, however, is destructive and limited to a small number of samples. In this study, the coupling of convolutional neural network (CNN) with machine vision was proposed in detecting the SSC of cantaloupe. The cantaloupe images were firstly acquired under controlled and uncontrolled conditions and subsequently fed to CNN to predict the class to each cantaloupe belongs. Four hand-crafted classical machine learning classifiers were used to compare against the performance of CNN.Experimental results showed that the CNN significantly outperformed others, with an improvement of more than 100% was achieved in terms of classification accuracy, considering the data acquired under the uncontrolled environment.The results demonstrated the potential benefit to operationalize the CNN in practice for the SSC determination of cantaloupe before harvesting. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.