Identifikasi Kematangan Buah Tomat (Solanum lycopersicum) Menggunakan YOLOv5n

Authors

  • Arya Yugo Udayana University image/svg+xml
  • I Putu Gede Budisanjaya Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia .
  • I Made Supartha Utama Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia .

DOI:

https://doi.org/10.24843/jbeta.2026.v14.i02.p17

Keywords:

deep learning, kematangan tomat, Raspberry Pi, YOLOv5, computer vision, deteksi objek

Abstract

Penelitian ini mengembangkan sistem deteksi buah tomat dan klasifikasi tingkat kematangan menggunakan algoritma YOLOv5n yang diimplementasikan pada perangkat Raspberry Pi 2. Dataset LaboroTomato yang terdiri dari 440 citra tomat jenis beef tomato digunakan dan diperluas melalui augmentasi menjadi 1.320 citra. Model dilatih selama 100 epoch menggunakan Google Colab dengan GPU NVIDIA Tesla T4. Evaluasi menunjukkan performa tinggi dengan precision 96,28%, recall 94,22%, F1-score 95,24%, dan mAP@0.5 sebesar 97,99%. Implementasi model dalam format ONNX meningkatkan kecepatan inferensi sebesar 20,5% dengan waktu rata-rata 1,47 detik per frame. Validasi lapangan oleh empat praktisi menghasilkan tingkat persetujuan sebesar 80,56%. Hasil ini menunjukkan bahwa YOLOv5n layak diterapkan pada perangkat embedded berdaya komputasi rendah untuk mendukung pengambilan keputusan panen tomat.

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Published

2026-09-30

Issue

Vol. 14 No. 2 (2026): September

Section

Articles

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Copyright (c) 2026 I Made Arya Yugo Satriawan, I Putu Gede Budisanjaya , I Made Supartha Utama

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How to Cite

Identifikasi Kematangan Buah Tomat (Solanum lycopersicum) Menggunakan YOLOv5n. (2026). Jurnal BETA (Biosistem Dan Teknik Pertanian), 14(2), 412-422. https://doi.org/10.24843/jbeta.2026.v14.i02.p17

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