Open Access

ARTICLE

Multi-Model CNN-RNN-LSTM Based Fruit Recognition and Classification

Harmandeep Singh Gill^1,*, Osamah Ibrahim Khalaf², Youseef Alotaibi³, Saleh Alghamdi⁴, Fawaz Alassery⁵

1 Department of Computer Science, Guru Arjan Dev Khalsa College Chohla Sahib, Punjab, 143408, India
2 Al-Nahrain University, Al-Nahrain Nano-Renewable Energy Research Center, Baghdad, 964, Iraq
3 Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
4 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
5 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Harmandeep Singh Gill. Email:

Intelligent Automation & Soft Computing 2022, 33(1), 637-650. https://doi.org/10.32604/iasc.2022.022589

Received 12 August 2021; Accepted 18 November 2021; Issue published 05 January 2022

Abstract

Contemporary vision and pattern recognition issues such as image, face, fingerprint identification, and recognition, DNA sequencing, often have a large number of properties and classes. To handle such types of complex problems, one type of feature descriptor is not enough. To overcome these issues, this paper proposed a multi-model recognition and classification strategy using multi-feature fusion approaches. One of the growing topics in computer and machine vision is fruit and vegetable identification and categorization. A fruit identification system may be employed to assist customers and purchasers in identifying the species and quality of fruit. Using Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) deep learning applications, a multi-model fruit image identification system was created. For performance assessment in terms of accuracy analysis, the proposed framework is compared to ANFIS, RNN, CNN, and RNN-CNN. The motivation for adopting deep learning is that these models categorize pictures without the need for any intervention or process. The suggested fruit recognition method offers efficient and promising results, according to the findings of the experiments in terms of accuracy and F-measure performance analysis.

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Citations