Application of Median Filter Method for Classification of Oil Palm Tree on LiDAR Images

This research discusses the problem of classifying oil palm trees that are widespread in Indonesian plantations using machine learning. The palm oil industry in Indonesia needs innovations to implement the plantation process more efficiently. This study focuses on testing the Median Filter preprocessing method in the classification process using the Convolutional Neural Network algorithm on light detection and ranging (LiDAR) format images. Researchers assume that a Median Filter that is able to remove salt and pepper noise in the image can help improve the quality of image clarity. Classification effectiveness was measured by evaluation of accuracy and F1-score. As a result, it can be concluded that the Median Filter cannot significantly affect the results of the LiDAR image classification of oil palm trees when compared to previous related studies. This is because the image data does not contain much salt and pepper noise. This study resulted in an accuracy of 84.98% and an F1-score of 0.85093 which is lower than the previous research without Median Filter preprocessing, which has an accuracy of 86%.