Automated System for Grading Apples using Convolutional Neural Network
Adigun J. O.2, Okikiola F. M.2, Aigbokhan, E. E.3, Rufai M. M.4
1Adigun J. O*, Department of Computer Technology, Yaba College of Technology, Lagos, Nigeria.
2Okikiola F. M., Department of Computer Technology, Yaba College of Technology, Lagos, Nigeria.
3Aigbokhan, E. E., Department of Computer Technology, Yaba College of Technology, Lagos, Nigeria.
4Rufai M. M., Department of Computer Technology, Yaba College of Technology, Lagos, Nigeria
Manuscript received on October 16, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 1458-1464 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4246119119/2019©BEIESP | DOI: 10.35940/ijitee.A4246.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fruit grading is a process that affect quality control and fruit-processing industries to meet the efficiency of its production and society. However, these industries have suffered from lack of standards in quality control, higher time of grading and low product output because of the use of manual methods. To meet the increasing demand of quality fruit products, fruit-processing industries must consider automating their fruit grading process. Several algorithms have been proposed over the years to achieve this purpose and their works were based on color, shape and inability to handle large dataset which resulted in slow recognition accuracy. To mitigate these flaws, we develop an automated system for grading and classification of apple using Convolutional Neural Network (CNN) used in image recognition and classification. Two models were developed from CNN using ResNet50 as its convolutional base, a process called transfer learning. The first model, the apple checker model (ACM) performs the recognition of the image with two output connections (apple and non-apple) while the apple grader model (AGM) does the classification of the image that has four output classes (spoiled, grade A, grade B & grade C) if the image is an apple. A comparison evaluation of both models were conducted and experimental results show that the ACM achieved a test accuracy of 100% while the AGM obtained recognition rate of 99.89%.The developed system may be employed in food processing industries and related life applications.
Keywords: Convolutional Neural Network, ResNet50, Artificial Neural Network, Machine learning, Computer vision.
Scope of the Article:Machine learning