Publication

Abstract : Ripening of banana hands during handling, on board transit, shipping and storage leads to higher post-harvest loss and impede the trade. Identification of ripening is paramount importance to reduce loss. Bulk handlers and food processing industries requires automated non-destructive methods of ripening stage identification methodologies. This paper proposes a deep learning based non-destructive method of classification of banana fruit under four categories – unripe, under ripe, ripe and over ripe. A customized dataset was prepared with sufficient images in each class. A convolution neural network (CNN) combined with an eXtreme Gradient Boosting (XgBoost) algorithm (CNN-XgBoost) is introduced for the effective determination of the ripening stage of banana. CNN acts as the trainable feature extractor of the images and XgBoost acts as the identifier of ripening stage. Linear Discriminant Analysis (LDA) is incorporated in order to eliminate the need to have data augmentation or a huge data set. Thus, the proposed deep learning approach possesses capability to perform classification even with a smaller data set compared to conventional deep and machine learning techniques. The performance accuracy of the proposed duo is found to be 91.25 % and it is higher than that obtained with a Support Vector Classifier (SVC), Gaussian Naive Bayesian Classifier (GNB) or k-Nearest Neighbours (KNN) algorithms.