Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning

Abstract

Palm oil is one of the world's highest vegetable oil producers. Providing accurate oil palm plantation statistics is essential to supporting effective and efficient decision-making, especially since the oil palm industry has a strategic role in achieving several Sustainable Development Goals (SDGs). The existing conventional data collection has been focusing on field survey methods which require considerable human resources and costs, long collection and processing times, and have difficulty in reaching remote areas. Remote sensing with satellite imagery and Unmanned Aerial Vehicles (UAV) can be an alternative due to its advantages which include a more efficient workforce, shorter update times, comprehensive area coverage, and the ability to reach remote locations. In this study, we propose the utilization of Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and UAV with image processing threshold and object-based deep learning methods to detect and count the number of oil palm trees. Experimental results in a case study in Rokan Hulu Regency, Riau, Indonesia show that the object-based deep learning model of the You Only Look Once (YOLO) architecture achieves excellent performance with an F1-Score of up to 91.05%. This model is superior to the widely adopted baseline model of the image processing threshold, which only reached an F1-Score of 40.59%. Our findings suggest the use of very high resolution images with object-based deep learning is promising for automatic detection and counting of oil palm trees to support the accurate and sustainable agricultural monitoring.