EXPLORE PUBLICATIONS BY COUNTRIES

Unmanned Aerial Vehicles (UAVs), Machine Learning, Oil Palm Plantations, Tree Detection, Tree Health Assessment.

The oil palm industry is a vital contributor to Malaysia’s economy but continues to face challenges in plantation monitoring and productivity management. Conventional approaches for tree counting and health assessment are labour-intensive, costly, and prone to errors. This study introduces a machine learning approach utilizing unmanned aerial vehicle (UAV) imagery to automate the detection and classification of oil palm tree health. UAV imagery was processed into a twodimensional orthomosaic map, which was analysed with trained deep learning models capable of identifying individual trees and categorising their health status. The results achieved an overall F1-score of 0.84, with healthy trees classified most accurately. Tree counting accuracy exceeded 90%, and precision–recall analysis demonstrated that the model maintained high precision at strong confidence thresholds, though threshold adjustments are required to optimise recall. Overall, the proposed model can reduce manual effort, time, and cost, while improving consistency in plantation monitoring. These findings highlight the potential of UAV-based digital agriculture solutions to support sustainable and data-driven oil palm management in Malaysia.