Deep Learning based Tree Counting Method for Various Plant Density in UAV-Captured Palm Oil Plantation Images

Abstract

Oil palm is a globally significant economic crop, especially in Malaysia and Indonesia, where it contributes substantially to national economies. Accurate monitoring of plantations, particularly tree count and health, is essential for effective management and yield estimation. However, traditional field-based methods are costly and labor-intensive, and while UAVs and very high-resolution satellite imagery offer precision, they are often limited by high cost, limited coverage, and technical constraints such as altitude variation and image mosaicking. This study proposes a scalable and cost-effective pipeline that utilizes UAV imagery implementing two tree-counting approaches Template Matching, serving as a classical baseline, and YOLOv8, a deep learning object detection model chosen for its high accuracy and inference speed. The oil palm tree density was classified into High, Medium, and Low category for easy result observation. Preliminary results demonstrate promising True Positive Rate (TPR), False Negative Rate (FNR), and Estimation Error (EE) values, with YOLOv8 achieving its best performance in medium-density regions (TPR: 109.54%, FNR: 9.50%, EE: 12.91 %) and Template Matching showing the weakest performance in low-density areas (TPR: 305.45%, FNR: 205.45 %, EE: 205.45 %). These results indicate that the proposed approach offers a practical, real-time, and resourceefficient solution for large-scale oil palm monitoring, although improvements are still required in low-density plantations.

Plant phenotyping relevance

UAV画像から油ヤシの樹木数・密度を推定する画像解析パイプラインを提案し、Template MatchingとYOLOv8を比較評価しており、植物個体数という観測可能な形態・群落特性の取得手法が中心である。

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