Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases

  • Candra Nur Ihsan Badan Riset dan Inovasi Nasional https://orcid.org/0000-0001-5751-9493
  • Nova Agustina Sekolah Tinggi Teknologi Bandung
  • Muchammad Naseer Sekolah Tinggi Teknologi Bandung
  • Harya Gusdevi Sekolah Tinggi Teknologi Bandung
  • Jack Febrian Rusdi Sekolah Tinggi Teknologi Bandung
  • Ari Hadhiwibowo Sekolah Tinggi Teknologi Bandung
  • Fahmi Abdullah Sekolah Tinggi Teknologi Bandung
DOI: https://doi.org/10.29207/resti.v8i1.5587
Keywords: Comparison, Machine Learning, Disease Detection, Tea Leaves

Abstract

Tea is one of the top ten export products sent from Indonesia to foreign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, although the value of the export impacts the country’s economic structure. In addition to market competition, Indonesia must maintain tea leaf production so that the increase in export decline is not significant or even increases tea leaf export production. To improve production quality and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tea leaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early. This study compares machine learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying tea leaf diseases are random forest (RF), support vector classifier (SVC), extra tree classifier (ETC), decision tree (DT), XGBoost classifier (XGB), and convolutional neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e., getting an average accuracy performance of 77.47%. Another algorithm, SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%, and the lowest is CNN of 59.08%. ETC has been proven to be the most superior machine learning algorithm for detecting tea leaf diseases in this study.

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Published
2024-02-18
How to Cite
Ihsan, C. N., Agustina, N., Naseer, M., Gusdevi, H., Rusdi, J. F., Hadhiwibowo, A., & Abdullah, F. (2024). Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(1), 135 - 141. https://doi.org/10.29207/resti.v8i1.5587
Issue
Vol 8 No 1 (2024): February 2024
Section
Information Technology Articles

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