A non‐destructive methodology for determination of cantaloupe sugar content using machine vision and deep learning
Pauline Ong, Suming Chen, Chao‐Yin Tsai, Yijing Wu, Yi‐Tzu Shen
Abstract
BACKGROUND: 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 practised. This method, however, is destructive and limited to a small number of samples. In this study, the coupling of a convolutional neural network (CNN) with machine vision was proposed in detecting the SSC of cantaloupe. The cantaloupe images were first acquired under controlled and uncontrolled conditions and subsequently fed to the CNN to predict the class to which each cantaloupe belonged. Four hand-crafted classical machine-learning classifiers were used to compare against the performance of the CNN. RESULTS: Experimental results showed that the CNN method significantly outperformed others, with an improvement of >100% being achieved in terms of classification accuracy, considering the data acquired under the uncontrolled environment. CONCLUSION: The results demonstrated the potential benefit to operationalize CNNs in practice for SSC determination of cantaloupe before harvesting. © 2022 Society of Chemical Industry.