Model Deteksi Pohon Kelapa Sawit dari Citra Drone dengan Pendekatan Deep Learning

Abstract

Indonesia merupakan produsen dan eksportir minyak sawit terbesar di dunia, oleh karena itu pentingnya pengelolaan perkebunan kelapa sawit yang baik demi keberlangsungan dan perkembangan komoditi kelapa sawit. Penghitungan pohon kelapa sawit (tree counting) merupakan praktik perkebunan yang penting dilakukan untuk inventarisasi aset biologis, estimasi produksi buah sawit, dan lain-lain. Penerapan pertanian presisi dalam penghitungan pohon kelapa sawit dapat diimplementasikan melalui deteksi pohon kelapa sawit dari citra udara. Penelitian ini menggunakan pendekatan deep learning algoritme YOLOv3, YOLOv4, dan YOLOv5m dalam mendeteksi pohon kelapa sawit dari citra drone. Dataset diperoleh dari PT Perkebunan Nusantara VI berupa citra drone pekebunan kelapa sawit di Jambi seluas 730 ha yang diberi 56.614 label kelas kelapa sawit. Pengujian deteksi pohon kelapa sawit dilakukan pada citra drone seluas 180 ha. Pohon kelapa sawit yang diteliti adalah tanaman muda (tahun tanam 2013–2015) dan tanaman dewasa (tahun tanam 2009–2011) pada areal mendatar dan berbukit dengan jarak antara kanopi tanaman jarang, berdekatan, dan tumpang tindih serta pohon kelapa sawit berdampingan dengan vegetasi lain. Tujuan dari penelitian ini membuat model menggunakan algoritme YOLOv3, YOLOv4, dan YOLOv5m dalam mendeteksi pohon kelapa sawit dari citra drone. Evaluasi model berupa perbandingan akurasi menggunakan confusion matrix dengan perhitungan Recall, Precision, dan F1-score, serta waktu deteksi dan presisi bounding box. Tahapan dari penelitian yaitu pengumpulan data, praproses data, pengembangan model, pengujian model, dan evaluasi model. Hasil pengujian terhadap 17.343 pohon kelapa sawit yang terbagi dalam 24 wilayah memperoleh F1-score untuk YOLOv3, YOLOv4, dan YOLOv5m masing-masing sebesar 97,28%, 97,74%, dan 94,94% dengan rata-rata waktu deteksi 43 detik, 45 detik, dan 21 detik untuk cakupan areal seluas 12 ha per wilayah yang mencapai 1000 pohon. Hal ini menunjukkan akurasi model sangat baik dan cepat dalam mendeteksi pohon kelapa sawit dan dapat diterapkan pada perusahaan perkebunan. Hasil dari penelitian ini diharapkan dapat membantu perusahaan perkebunan dalam inventarisasi pohon kelapa sawit dengan akurasi tinggi dan efisien dalam skala besar. Dengan pelaksanaan inventarisasi pohon yang tepat menjadi dasar untuk pengelolaan perkebunan kelapa sawit yang lebih baik dan manajemen dapat menjadikannya sebagai dasar penggunaan biaya.

Indonesia is the largest producer and exporter of palm oil in the world, therefore the importance of good oil palm plantation management for the sustainability and development of the palm oil commodity. Tree counting is an important plantation practice for biological asset inventories, fresh fruit bunch production estimation, etc. The application of precision agriculture in counting oil palm trees can be implemented by the detection of oil palm trees from aerial imagery. This study uses a deep learning approach using YOLOv3, YOLOv4, and YOLOv5m in detecting oil palm trees from drone images. The dataset was obtained from PT Perkebunan Nusantara VI in the form of drone images of oil palm plantations in Jambi covering an area of 730 ha which were given 56,614 class labels for oil palm. The detection test of oil palm trees was carried out on drone images covering an area of 180 ha. The oil palm trees studied were young plants (the planting year 2013–2015) and mature plants (the planting year 2009–2011) in flat and hilly areas with sparse, close, and overlapping distances between plant canopy, and oil palm trees intersect with other vegetations. The purpose of this study is to create a model using the YOLOv3, YOLOv4, and YOLOv5m algorithms in detecting oil palm trees from drone images. The evaluation of the model is in the form of a comparison of accuracy using a confusion matrix with Recall, Precision, and F1-score calculations, as well as detection time and bounding box accuracy. The stages of the research are data collection, data preprocessing, model development, model testing, and model evaluation. Model testing using images from 24 regions, each of which covers 12 ha with up to 1000 trees (for a total of 17,343 oil palm trees) yielded F1-scores of 97.28%, 97.74%, and 94.94%, with an average detection time of 43 seconds, 45 seconds, and 21 seconds for models trained with YOLOv3, YOLOv4, and YOLOv5m, respectively. This result shows that the method is sufficiently accurate and efficient in detecting oil palm trees and has the potential to be implemented in commercial applications for plantation companies. The results of this study are expected to help plantation companies in inventorying oil palm trees with high accuracy and efficiency on a large scale. With proper implementation of tree inventories, it becomes the basis for better management of oil palm plantations and management can use this as the basis for using costs.