Comparison of Machine Learning Algorithms in Detecting Tea LeafDiseases

Comparison of Machine Learning Algorithms in Detecting Tea LeafDiseases

comparison;machine learning;disease detection;tea leaves

Tea is one of the top ten most exported products sent from Indonesia toforeign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, even though the export value impacts the country’s economic structure. Besides market competition, Indonesia needs to maintain tea leaf production sothat the spike in export decline is not significant or even increases the export production of tea leaves. To improve the quality of production and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tealeaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early on. This study compares Machine Learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying the 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, i.e.,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 is proven to be the most superior Machine Learning algorithm for detecting tea leaf diseases in this study.

Candra Nur Ihsan1, Nova Agustina2,Muchammad Naseer3, Harya Gusdevi4,Jack Febrian Rusdi5, Ari Hadhiwibowo6, Fahmi Abdullah

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5587/901

Department of Informatics, Sekolah Tinggi Teknologi Bandung,Bandung,Indonesia

18-02-2024

FAJAR BAGUS W

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