Published: 2026-04-01

Deteksi Penyakit Daun Teh Berdasarkan Citra Menggunakan Deep Learning

DOI: 10.35870/jtik.v10i2.5657

Andreas Saputra, Dedy Hermanto
  • Andreas Saputra: Universitas Multi Data Palembang
  • Dedy Hermanto: Universitas Multi Data Palembang
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Abstract

Tea plant (Camellia sinensis) originates from China and is one of the most widely consumed beverages in the world. Tea plants are vulnerable to leaf diseases such as Tea Leaf Blight, Tea Red Leaf Spot, and Tea Red Scab, which can reduce the quality and productivity of the harvest. Manual disease identification is still commonly used, but this method has many limitations, such as dependence on farmers’ experience and inaccuracy in early detection. This study aims to apply the YOLOv11 algorithm as an object detection method to automatically, quickly, and accurately detect four classes of tea leaf conditions (three diseases and one healthy). The dataset used consists of 3,960 high-resolution tea leaf images that have undergone segmentation, augmentation, and normalization processes. The research was carried out through image preprocessing, YOLOv11 model training, and model performance evaluation using precision, recall, F1-score, and mean Average Precision (mAP) metrics. The results of tea leaf disease detection using YOLOv11 achieved an average precision of 97.2%, recall of 98.2%, mAP@0.5 of 98.8%, and mAP@0.5:0.95 of 95.5%. This model can be used to help farmers identify tea leaf diseases more quickly and reduce the risk of crop yield losses.

Keywords

Tea Leaves ; Disease Detection ; YOLOv11 ; Object Detection ; mAP

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How to Cite

Saputra, A., & Hermanto, D. (2026). Deteksi Penyakit Daun Teh Berdasarkan Citra Menggunakan Deep Learning. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 10(2), 643-652. https://doi.org/10.35870/jtik.v10i2.5657
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 10 No. 2 (2026)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2026

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Author Biographies
Andreas Saputra
Google Scholar ResearchGate ORCID

Jurusan Informatika, Fakultas Ilmu Komputer dan Rekayasa, Universitas Multi Data Palembang, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.

Dedy Hermanto
Google Scholar ResearchGate ORCID

Jurusan Informatika, Fakultas Ilmu Komputer dan Rekayasa, Universitas Multi Data Palembang, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.

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