Deep learning based intelligent identification system for ripening stages of banana

作     者：Shuprajhaa, Thayumanavan Raj, J. Mathav Paramasivam, Suresh Kumar Sheeba, K. N. Uma, Subbaraya 

作者机构：ICAR Natl Res Ctr Banana Tiruchirappalli 620102 Tamil Nadu India Indian Inst Technol Gauhati 781039 Assam India Natl Inst Technol Tiruchirappalli 620015 Tamil Nadu India 

出 版 物：《POSTHARVEST BIOLOGY AND TECHNOLOGY》 (采收后生物学和技术)

年 卷 期：2023年第203卷第1期

核心收录：

学科分类：0832[工学-食品科学与工程（可授工学、农学学位）] 09[农学] 0901[农学-作物学] 0902[农学-园艺学] 

基　　金：ICAR - National Agricultural Science Fund, NASF Indian Council of Agricultural Research, ICAR 

主　　题：Convolution Neural Network XgBoost algorithm Ripeness Supply chain Post-harvest loss Machine learning 

摘      要：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 meth-odologies. 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.