Optimizing Oil Palm Tree Inventory Management with Advanced Drone-Based Image Recognition Techniques

Abstract

Oil palm tree plantations are a crucial source of vegetable oil for various industries, playing a significant role in the global economy. One key aspect of plantation management is accurate inventory management of oil palm trees, which involves the detection and counting of individual trees. Traditional inventory management methods for oil palm trees rely on ground-based manual measurements, which are time-consuming, labor-intensive, and prone to errors. However, accurate and efficient detection and counting of oil palm trees from drone images remain challenging due to the complex and variable nature of the plantation environment. Drone-based remote sensing has emerged as a promising alternative for inventory management in recent years. In this study, a novel approach to enhance the accuracy and efficiency of oil palm tree detection and counting using advanced drone-based image recognition techniques is proposed. The research discusses a novel image recognition technique that uses a custom GLCM, Haar Wavelet, and template matching to detect and count oil palm trees from drone images. The proposed approach outperformed traditional machine learning techniques and achieved a high accuracy of 83.75% and 86.9% in detecting individual oil palm trees in Jeli and Keratong, respectively. Haar Wavelet proves this algorithm achieves the highest overall accuracy. Additionally, eight statistical parameters can manipulate the GLCM, while offset parameters can boost accuracy for various applications or methods. The study highlights the potential of advanced drone-based image recognition techniques for optimizing plantation management and contributing to sustainable production practices.