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Recognition and Classification of Fruits using Deep Learning Techniques

Kathepuri, Shubham (2020) Recognition and Classification of Fruits using Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Recently, there have been great advancements in the field of deep learning making it a popular choice for image processing applications. Recognition and classification of fruits using deep learning is one of the exciting applications of computer vision for commercial as well as agricultural applications. Nevertheless, the researchers still face challenges while the classification of fruits due to similarity of color, shape, and size. This project attempts to address some of the challenges faced by the previous researchers by developing a methodology for the recognition and classification of fruits. The deep learning models used for the project were CNN, VGG16, and ResNet50. These models were trained and tested using two sets of data one was pre-processed and the other was augmented. CNN performed well on the first set of data with high accuracy of 0.9691 and less computational time of 11.8 minutes whereas ResNet50 was able to achieve high accuracy of 0.9522 on the second set of data with a computational time of 24.87 minutes. Hence, the deep learning models were able to recognize and classify 131 categories of fruits accurately. However, it was observed that image augmentation did not improve the performance of the models.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 22 Jan 2021 14:36
Last Modified: 22 Jan 2021 14:36
URI: http://norma.ncirl.ie/id/eprint/4448

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