An integrated approach using CNN-RNN-LSTM for classification of fruit images. (2022)

• Record Type: Journal Article
• Authors: Gill, Harmandeep Singh; Khehra, Baljit Singh
• Is Part Of: Materials today. Volume 51:Part 1(2022)
• Page Start: 591
• Page End: 595
• Publication Date: 2022
• Subjects: CNN -- RNN -- LSTM -- Integrated Approach -- Fruit classification; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.06.016
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: With the advancement in technology, Computer and machine vision system is getting involved in the agriculture sector for the last few years. Deep Learning is a recent advancement in the Artificial Intelligence field. In the present era, many researchers have used deep learning applications for the classification of images, and is found to be one of the emerging areas in computer vision. In the classification of fruit images, the main goal is to improve the accuracy of the classification system. The accuracy of the classifier depends on various factors like the nature of acquired images, the number of features, types of features, selection of optimal features from extracted features, and type of classifiers used. In the proposed article, integration of CNN, RNN, and LSTM for the classification of fruit images are defined. In this approach, CNN and RNN are employed for the development of discriminative characteristics and sequential-labels respectively. LSTM presents an explanation by integrating a memory cell to encode learning at each interval of classification. Key parameters: accuracy, F-measure, sensitivity, and specificity are applied to assess the achievement of the proposed scheme. From empirical results, it has been declared that the offered classification method provides efficient results.