The Classification of Tea Leaf Diseases Using Sift Feature Extraction of Learning Vector Quantization Method with Support Vector Machine

Mutia Ulfa, Mutia Ulfa, Rahmad Syah, Rahmad Syah, Muhathir, Muhathir

2023

Abstract

Productivity is highly dependent on healthy leaves, which are the main components of the product. However, plants are very susceptible to all kinds of disturbances. One of these disturbances is a pest that causes disease on tea leaves; the pest is helopeltis. is a type of pest that attacks young leaf shoots by piercing the part to be attacked, and then the puncture mark from the razor will show symptoms in the form of irregular spots. Based on the uniqueness of the damage pattern on the tea leaves, this study tested the classification of the types of tea leaf diseases by comparing two methods, namely support vector machine and learning vector quantization, and utilizing SIFT feature extraction. The level of accuracy produced by each method is 98% using the Support Vector Machine method with 99% precision, 98% recall, and 98% F1-Score, and 94% using the Learning Vector Quantization method with 96% precision, 94% recall, and 96% F1-Score.

Paper Citation

in Harvard Style

Ulfa M., Syah R. and Muhathir. (2023). The Classification of Tea Leaf Diseases Using Sift Feature Extraction of Learning Vector Quantization Method with Support Vector Machine. In Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD; ISBN 978-989-758-678-1, SciTePress, pages 10-14. DOI: 10.5220/0012439900003848

in Bibtex Style

@conference{icaisd23,
author={Mutia Ulfa and Rahmad Syah and Muhathir},
title={The Classification of Tea Leaf Diseases Using Sift Feature Extraction of Learning Vector Quantization Method with Support Vector Machine},
booktitle={Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD},
year={2023},
pages={10-14},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012439900003848},
isbn={978-989-758-678-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD
TI - The Classification of Tea Leaf Diseases Using Sift Feature Extraction of Learning Vector Quantization Method with Support Vector Machine
SN - 978-989-758-678-1
AU - Ulfa M.
AU - Syah R.
AU - Muhathir.
PY - 2023
SP - 10
EP - 14
DO - 10.5220/0012439900003848
PB - SciTePress