Classification of Cassava (Manihot sp.) Leaf Variants Using Transfer Learning

Authors

Agus Pratondo
Telkom University image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v11i2.4685
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Published: 15-06-2023 • Issue: Vol. 11 No. 2: June 2023 • Section: Computer_&_Informatics

Abstract

There are several types of cassava leaves with different characteristics, tastes, and nutritional content. Some people use cassava leaves as a vegetable ingredient for daily consumption as a source of fiber and minerals. However, people often have difficulty identifying the different types of cassava leaves, including cassava leaf variants that are locally referred to as gajah, karet, and mentega. This study aims to use transfer learning to identify the variant of cassava leaves. The Inception v3 architecture was selected to build the classification model. To demonstrate the superiority of transfer learning, the Inception v3 architecture was run with two different weights. The first weight was randomly initialized, while the second weight was taken from pre-trained weights from ImageNet. The experimental results show that the classification accuracy rate using the pre-trained weights reached 95.76%. This indicates that the classification model used in this study is promising and can be used for practical purposes in everyday life.
Keywords: cassava leaves gajah karet metega inception v3 transfer learning

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Author Biographies

Agus Pratondo
Agus Pratondo is an accomplished researcher and academic who holds a bachelor's degree in Informatics from the Bandung Institute of Technology. After working as a software engineer and tester for several years, he obtained a Master's degree in Information Technology from the same institution in 2008. Agus began his academic career in the Department of Information Systems at Telkom University and later received his Ph.D. in the Department of Electrical and Computer Engineering from the National University of Singapore (NUS) in 2016. He is currently an associate professor in the Department of Multimedia Engineering at Telkom University. Agus's research interests are focused on computer vision, medical image segmentation, and machine learning. He is a senior member of IEEE and has published extensively in academic journals and conferences, demonstrating his expertise in these fields. Agus can be contacted at {firstname}.{lastname}.id@ieee.org.

How to Cite

[1]
A. Pratondo, “Classification of Cassava (Manihot sp.) Leaf Variants Using Transfer Learning”, IJEEI, vol. 11, no. 2, pp. 442–452, Jun. 2023, doi: 10.52549/ijeei.v11i2.4685.