SISTEM KLASIFIKASI KESEHATAN PEPAYA (Carica Papaya L.) MENGGUNAKAN YOLO11x BERBASIS UNMANNED AERIAL VEHICLE VERTICAL TAKE-OFF LANDING

Fahmi Arsyad; Ardan Wiratmoko; Mutiara Alifia Ramadhanty; Andri Prima Nugroho; Lilik Sutiarso; Takashi Okayasu

doi:10.35760/jpp.2026.v10i1.260

Authors

Fahmi Arsyad Universitas Gadjah Mada Author
Ardan Wiratmoko Universitas Gadjah Mada Author
Mutiara Alifia Ramadhanty Universitas Gadjah Mada Author
Andri Prima Nugroho Universitas Gadjah Mada Author
Lilik Sutiarso Universitas Gadjah Mada Author
Takashi Okayasu Kyushu University Author

DOI:

https://doi.org/10.35760/jpp.2026.v10i1.260

Keywords:

Citra Udara, Klasifikasi kesehatan tanaman, Pertanian presisi, UAV VTOL, Deteksi objek

Abstract

Klasifikasi kesehatan tanaman pepaya (Carica papaya L.) merupakan aspek penting dalam mendukung pertanian presisi karena informasi tersebut dapat dimanfaatkan untuk pemantauan kondisi tanaman, estimasi produktivitas, serta pengambilan keputusan manajemen kebun secara tepat. Metode konvensional yang mengandalkan observasi visual masih sering digunakan, tetapi kurang efisien pada skala luas dan berpotensi menimbulkan subjektivitas. Penelitian ini mengembangkan sistem klasifikasi kesehatan tanaman berbasis computer vision menggunakan algoritma deep learning YOLOv11 yang dilatih dari citra udara beresolusi tinggi hasil akuisisi UAV VTOL. Tahapan penelitian meliputi pengambilan data citra, pembuatan orthomosaic, segmentasi area of interest, tiling, anotasi ke dalam tiga kelas kesehatan yaitu Normal, Moderate, dan Abnormal, serta augmentasi hingga diperoleh 541 citra. Model dilatih selama 100 epoch dengan ukuran citra 1024 × 1024 piksel dan dievaluasi menggunakan precision, recall, F1-score, serta mean Average Precision. Hasil evaluasi menunjukkan bahwa model mencapai precision 0,801, recall 0,821, F1-score 0,811, dan mAP50 0,854 yang mengindikasikan kinerja yang baik. Meskipun demikian, kelas Moderate masih menjadi keterbatasan karena kemiripan karakteristik visual dengan kelas lainnya. Sistem ini mampu menghasilkan visualisasi distribusi spasial kesehatan tanaman yang informatif untuk mendukung implementasi pertanian presisi.

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Author Biographies

Fahmi Arsyad, Universitas Gadjah Mada

Researcher affiliated with the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada.
Ardan Wiratmoko, Universitas Gadjah Mada

Lecturer and researcher at the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada.
Mutiara Alifia Ramadhanty, Universitas Gadjah Mada

Researcher affiliated with the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada.
Andri Prima Nugroho, Universitas Gadjah Mada

Academic and researcher at the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada.
Lilik Sutiarso, Universitas Gadjah Mada

Academic and researcher at the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada
Takashi Okayasu, Kyushu University

Academic and researcher at the Department of Agro-Environmental Science, Faculty of Agriculture, Kyushu University

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