Oil palm trees detection and counting on Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicles (UAV) data using image processing thresholding approach

Abstract

Palm oil is one of the highest producing vegetable oil crops globally, with production increasing rapidly over the last 40 years from 5 million tonnes in 1980 to 74.5 million tonnes in 2019. This increase in production is in line with the high demand for vegetable oils worldwide. The accurate monitoring and statistics of oil palm plantation data are essential to support effective and efficient decision-making. However, the most commonly adopted data collection still uses conventional methods, i.e., field surveys, which are highly dependent on the massive amount of human resources, cost, processing time, and difficulty reaching remote areas. Remote sensing using satellite and UAV imagery can be an alternative in data collection due to its distinct advantages with a more efficient labor force, affordable cost, shorter time updates, and covering areas that are difficult to reach. In this work, we investigate the utilization of remote sensing data from Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicle (UAV) data using the image processing thresholding method for detecting and counting oil palm trees. The combination of Hue, Saturation, and Value (HSV) conversion, the Otsu segmentation thresholding, and contours detection and counting methods are used in our approach to enhance the accuracy of captured features of oil palm trees. The detection results are further categorized into best-case, average-case, and worst-case detection to comprehend the challenging real-world situation, based on the quality of captured imageries and model prediction results. Our proposed approach achieves better and more promising results when using UAV image data. This is indicated by an average true-positive rate (TPR) of 88 to 97% in best-case data input, 70 to 81% in average-case data input, 30 to 53% in worst-case data input, and 10% in estimation error. Meanwhile, Bing image data provides an average true-positive rate (TPR) of 9 to 13% in the best-case data input, 5 to 6% in the average-case data input, and 1 to 3% in the worst-case data input, with an average estimation error up to 78%. Overall, our proposed approach can perform the oil palm trees detection and counting. We also suggest that higher resolution imagery than Microsoft Bing maps or single utilization of UAV data can be considered input for better results. Our study could further be beneficial in providing more scalable and accurate plantation statistics.