Deteksi Tanaman Kelapa Sawit Belum Menghasilkan pada Citra Udara Beresolusi Tinggi menggunakan Metode Deep Learning
DOI:
https://doi.org/10.26418/jp.v11i3.102052Keywords:
Deteksi Objek, YOLOv11m, SAHI, Citra Udara, Kelapa SawitAbstract
Deteksi dan perhitungan jumlah pohon kelapa sawit merupakan langkah penting dalam mendukung pemantauan produktivitas perkebunan. Penelitian ini menerapkan model YOLOv11m yang dikombinasikan dengan framework Slicing Aided Hyper Inference (SAHI) pada citra udara beresolusi tinggi yang diperoleh dari Unmanned Aerial Vehicle (UAV). Penelitian ini terdiri dari tiga tahap, yaitu pengumpulan data citra udara dan pelabelan, pelatihan model YOLOv11m dengan penyesuaian hyperparameter, serta pengujian model menggunakan metode slicing dari SAHI untuk meningkatkan kemampuan deteksi terhadap objek kecil. Berdasarkan hasil pengujian, model berhasil mendeteksi sebanyak 1.784 pohon kelapa sawit dari total 2.115 pohon aktual, dengan 10 deteksi salah dan 321 pohon tidak terdeteksi, menghasilkan nilai Mean Absolute Percentage Error (MAPE) sebesar 15,66%. Hasil ini menunjukkan bahwa kombinasi YOLOv11m dan SAHI mampu memberikan performa yang cukup baik dalam mendeteksi pohon kelapa sawit belum menghasilkan pada citra udara beresolusi tinggi, serta berpotensi diterapkan dalam proses pemantauan dan perhitungan pohon secara otomatis pada area perkebunan yang luas.References
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