Artikel Penelitian Klasifikasi Penyakit Early Blight dan Late Blight pada Daun Tomat dengan YOLOv8

Dodi Dwi Riskianto Dwi; Ahmad Rico Mardiansyah; Ahmad Supriadi

doi:10.30787/restia.v4i2.2509

Penulis

Dodi Dwi Riskianto Dwi Universitas Nurl Jadid
Ahmad Rico Mardiansyah Universitas Nurul Jadid, Probolinggo, Indonesia
Ahmad Supriadi Universitas Nurul Jadid, Probolinggo, Indonesia

DOI:

https://doi.org/10.30787/restia.v4i2.2509

Kata Kunci:

YOLOv8, Penyakit Tumbuhan, Pertanian Tomat, Deep Learning, Computer Vision

Abstrak

Penyakit pada daun tomat merupakan masalah utama dalam pertanian di Indonesia yang dapat menurunkan hasil panen. Penelitian ini bertujuan mengembangkan sistem deteksi penyakit daun tomat menggunakan YOLOv8, model deteksi objek yang cepat dan akurat. Dataset diklasifikasikan ke dalam beberapa kelas, seperti Healthy, Bacterial Spot, Early Blight, dan black spot, dengan proses augmentasi data dan normalisasi untuk meningkatkan performa. Evaluasi dilakukan menggunakan kurva F1, akurasi, dan matriks kebingungan. Hasil menunjukkan model YOLOv8 mencapai nilai F1 rata-rata 0.72 pada confidence optimal 0.506, dengan performa terbaik pada kelas Healthy. Namun, kelas seperti Bacterial Spot dan black spot menunjukkan tingkat kesalahan tinggi akibat tumpang tindih fitur antar kelas. Analisis matriks kebingungan mengindikasikan perlunya peningkatan distribusi data dan fitur representatif untuk meningkatkan akurasi. Penelitian ini menunjukkan potensi YOLOv8 untuk deteksi penyakit daun tomat secara real-time. Optimalisasi melalui balancing dataset, augmentasi lebih beragam, dan fine-tuning model diperlukan untuk meningkatkan sensitivitas sistem. Sistem ini memiliki potensi besar untuk diterapkan pada perangkat berbasis IoT, Guna mendukung kemajuan pertanian yang berada di Indonesia.

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