TINJAUAN LITERATUR SISTEMATIS PENDEKATAN DEEP LEARNING UNTUK DETEKSI DAN PERHITUNGAN POHON KELAPA SAWIT

Raden Gafur Wijayanto
Universitas Budi Luhur
Indonesia
Reza Fauzi
Universitas Budi Luhur
Indonesia
Anton Satria Prabuwono
Universitas Budi Luhur
Indonesia

DOI: http://dx.doi.org/10.35760/ik.2025.v30i1.14007

Article Submitted: 13 February 2025

Article Published: 02 May 2025

Abstract
Kelapa sawit merupakan komoditas utama dalam industri perkebunan yang membutuhkan manajemen efektif, terutama dalam pendeteksian dan penghitungan pohon guna meningkatkan produktivitas dan efisiensi operasional. Pendekatan manual memiliki keterbatasan dalam akurasi dan efisiensi, sehingga deep learning menjadi solusi yang menjanjikan. Penelitian ini menerapkan metode Systematic Literature Review (SLR) untuk mengidentifikasi teknik terbaru dalam deteksi dan penghitungan pohon kelapa sawit menggunakan citra penginderaan jauh. Dari 15 artikel yang dianalisis (2019–2024), berbagai metode ditemukan, termasuk Multi-level Attention Domain Adaptation Network (MADAN), Multi-class Oil Palm Detection Approach (MOPAD), YOLO, CNN, ANN, dan ResNet, dengan sumber data dari Google Earth, citra satelit, serta UAV. YOLOv4 mencatat F1-Score tertinggi 97,74%, sedangkan ANN mencapai akurasi 98,29%. Pemanfaatan UAV terbukti meningkatkan akurasi deteksi dibandingkan citra satelit. Tantangan utama meliputi kebutuhan dataset berkualitas tinggi, variabilitas kondisi lingkungan, serta integrasi ke dalam sistem manajemen perkebunan. Studi ini menyimpulkan bahwa deep learning efektif dalam otomatisasi deteksi dan penghitungan pohon kelapa sawit, namun optimalisasi model dan pengembangan teknik baru masih diperlukan untuk meningkatkan akurasi serta penerapan dalam skala industri guna mendukung pertanian presisi dan keberlanjutan sektor perkebunan
Keywords
Perhitungan; Deep learning; Detection;Pohon Kelapa Sawit; Systematic Literature Review
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